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Towards Robust Text Classification with Semantics-Aware Recurrent Neural Architecture


Abstract and Figures

Deep neural networks are becoming ubiquitous in text mining and natural language processing, but semantic resources, such as taxonomies and ontologies, are yet to be fully exploited in a deep learning setting. This paper presents an efficient semantic text mining approach, which converts semantic information related to a given set of documents into a set of novel features that are used for learning. The proposed Semantics-aware Recurrent deep Neural Architecture (SRNA) enables the system to learn simultaneously from the semantic vectors and from the raw text documents. We test the effectiveness of the approach on three text classification tasks: news topic categorization, sentiment analysis and gender profiling. The experiments show that the proposed approach outperforms the approach without semantic knowledge, with highest accuracy gain (up to 10%) achieved on short document fragments.
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machine learning &
knowledge extraction
Towards Robust Text Classification with
Semantics-Aware Recurrent Neural Architecture
Blaž Škrlj 1,2, Jan Kralj 1, Nada Lavraˇc 1,3 and Senja Pollak 1,4,*
1Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (B.Š.); (J.K.); (N.L.)
2Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
3School of Engineering and Management, University of Nova Gorica, 5000 Nova Gorica, Slovenia
4Usher Institute, Medical School, University of Edinburgh, Edinburgh EH16 4UX, UK
Received: 21 February 2019; Accepted: 1 April 2019; Published: 4 April 2019
Deep neural networks are becoming ubiquitous in text mining and natural language
processing, but semantic resources, such as taxonomies and ontologies, are yet to be fully exploited
in a deep learning setting. This paper presents an efficient semantic text mining approach, which
converts semantic information related to a given set of documents into a set of novel features
that are used for learning. The proposed Semantics-aware Recurrent deep Neural Architecture
(SRNA) enables the system to learn simultaneously from the semantic vectors and from the raw text
documents. We test the effectiveness of the approach on three text classification tasks: news topic
categorization, sentiment analysis and gender profiling. The experiments show that the proposed
approach outperforms the approach without semantic knowledge, with highest accuracy gain (up to
10%) achieved on short document fragments.
recurrent neural networks; text mining; semantic data mining; taxonomies; document
1. Introduction
The task of classifying data instances has been addressed in data mining, machine learning,
database, and information retrieval research [
]. In text mining, document classification refers to
the task of classifying a given text document into one or more categories based on its content [
]. A
text classifier is given a set of labeled documents as input, and is expected to learn to associate the
patterns appearing in the documents to the document labels. Lately, deep learning approaches have
become a standard in natural language-related learning tasks, showing high performance in different
classification tasks involving various text types, including sentiment analysis of tweets [
] and news
categorization [4].
Semantic data mining denotes a data mining approach where (domain) ontologies are used
as background knowledge in the data mining process [
]. Semantic data mining approaches have
been successfully applied in semantic subgroup discovery [
], data visualization [
], as well as text
classification [
]. Provision of semantic information allows the learner to use features on a higher
semantic level, allowing for data generalization. The semantic information is commonly represented
as relational data in the form of networks or ontologies. Even though there are many sources of
such knowledge, approaches capable of leveraging such information in a deep learning setting are
still scarce.
This paper proposes a novel approach where semantic information in the form of taxonomies (i.e.,
ontologies with only hierarchical relations) is propositionalized and then used in a recurrent neural
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network architecture. The proposed SRNA (Semantics-aware Recurrent Neural Architecture) approach
has been tested on a document classification task, while special attention is paid to the robustness of the
method on short document fragments. Classification of short or incomplete documents is useful in a
large variety of tasks. For example, in author profiling, the task is to recognize author’s characteristics,
such as age or gender [
], based on a collection of author’s text samples, where the effect of data size
is known to be an important factor influencing classification performance [
]. A frequent text type for
this task are tweets, where a collection of tweets from the same author is considered a single document,
to which a label must be assigned. The fewer instances (tweets) we need, the more powerful and
useful is the approach. In a similar way, this holds true for nearly any kind of text classification task.
For example, for labeling a news article with a topic tag, using only snippets or titles and not the entire
news, may be preferred due to limited text availability or required processing speed.
It has been demonstrated that deep neural networks need a large amount of information in order
to learn complex representations from text documents, and that state-of-the-art models do not perform
well when incomplete information is used as input [
]. This work addresses an open problem of
increasing the robustness of deep neural network-based classifiers in such settings by exploring to
what extent the documents can be truncated without affecting the learner’s performance.
This work is structured as follows. Section 2presents the background and related work. Section 3
introduces the proposed SRNA architecture, where semantic information in the form of taxonomies is
propositionalized and used in a recurrent neural architecture. Sections 4and 5present the experimental
setup and results of the evaluation on three publicly available data sets, with a special focus on how
the constructed semantic vectors affect the classifier’s performance. We conclude the paper in Section 6
with the plans for further work.
2. Background and Related Work
This section outlines the background and the related work in semantics-aware data mining and
deep learning architectures.
2.1. Document Representation and Semantic Context
Document classification is highly dependent on document representation. In simple bag-of-words
representations, the frequency (or a similar weight such as term frequency inverse document frequency)
of each word or
-gram is considered as a separate feature. More advanced representations group
words with similar meaning together. The approaches include Latent Semantic Analysis (LSA) [
Latent Dirichlet Allocation (LDA) [
], and more recently word embeddings [
], which transform
data instances (documents) into feature vectors in a lower-dimensional numeric vector space. One
of the well known algorithms for word embedding is word2vec [
], which uses a two-layer shallow
neural network architecture to capture the word context of the given text. As word2vec captures
limited contextual information, recently introduced embedding approaches such as GloVe [
] and
FastText [
] attempt to address these issues. Individual embeddings (feature vectors) are positioned
closer if they are contextually more similar. Both embedding and LSA-based approaches have
significantly improved in the recent years, both in terms of scalability, as well as in terms of their
predictive power [18,19].
It has been previously demonstrated that context-aware algorithms significantly outperform the
naive learning ones [
]. Neural networks can learn word representations by using their context, and
are as such especially useful for text classification tasks. We refer to such semantic context as the
first-level context.
Second-level context can be introduced by incorporating extensive amounts of background knowledge
(e.g., in the form of ontologies or taxonomies) into a learning task, which can lead to improved
performance of semantics-aware rule learning [
], subgroup discovery [
], and random forest
learning [
]. In text mining, Elhadad et al. [
] report an ontology-based web document classifier,
Mach. Learn. Knowl. Extr. 2019,1577
while Kaur et al. [
] propose a clustering-based algorithm for document classification, which also
benefits from the knowledge stored in the underlying ontologies.
Cagliero and Garza [
] report a custom classification algorithm, which can leverage taxonomies,
and demonstrate—on a case study of geospatial data—that such information can be used to
improve classification. Use of hypernym-based features for classification tasks has been considered
previously. The Ripper rule learner was used with hypernym-based features [
], while the
impact of WordNet-based features for text classification was also evaluated [
], demonstrating that
hypernym-based features significantly impact the classifier performance.
Even though including background information in deep learning has yet to be fully exploited,
there are already some semantic deep learning approaches available for text classification. Tang et al.
] have demonstrated that word embedding approaches can take into account semantics-specific
information to improve classification. Ristoski et al. [
] show that embedding-based approaches
are useful for taxonomy induction and completion. Liu et al. [
] address incorporation of
taxonomy-derived background knowledge as a constrained optimization problem, demonstrating
that semantic information can be valuable for the tasks of entity recognition and sentence completion.
Finally, Bian et al. [
] leverage morphological, syntactic, and semantic knowledge to achieve
high-quality word embeddings and prove that knowledge-powered deep learning can enhance their
2.2. Deep Learning Architectures
This section introduces deep learning architectures for text classification.
A two-layer neural network has been introduced as part of the word2vec embedding approach [
Recently, deeper architectures have proven to work well in document classification tasks [
], where
a neural network is given a set of vectors, whose elements are e.g., individual word indexes that are
directly used to produce class predictions. These approaches include convolutional neural networks,
which have been previously proven to work well for image classification [
]. A convolution is
defined as:
s(t) = (xw)(t) =
where xis the input function, mthe input vector dimensionality and wis a kernel.
Kernels are smaller sub-matrices, which are applied in the process of convolution, and result in a
modified origin matrix that can represent e.g., an image or a text sequence.
A convolutional neural network consists of at least three different types of computational layers:
a convolution layer, a pooling layer, and a dense fully connected layer. The convolution layer returns
convolutions computed on the given (single or multidimensional) inputs. Such a layer is normally
followed by a pooling layer. Here, sets of neurons’ outputs are merged into a single real number
Common pooling layers include maximum and average pooling. Finally, the fully connected layer
consists of a set of neurons, such that each neuron in the fully connected layer is connected with each
neuron in the previous layer. In most contemporary convolutional architectures, fully connected layers
(the first types of layers to be used in neural networks) are only used in the final stages due to their
prohibitive computational cost. Single-dimensional convolutional networks are used extensively in
natural language processing (NLP) tasks [
]. In a standard setting, vectors of word indexes are
used as input for a deep learning-based text classifier. The first layer in such architectures is responsible
for the construction of a lower-dimensional word index embedding, which is further used for learning.
The objective of this layer is to project the high dimensional input into a lower dimensional vector
space, more suitable for computationally expensive learning [34].
Recently, recurrent neural networks have gained significant momentum [
]. A recurrent neural
network is a type of architecture with recurrent connections between individual neurons. Similarly
to feedback loops in biology, such architectures to some extent enable memory storage. The most
Mach. Learn. Knowl. Extr. 2019,1578
commonly used recurrent architecture for sequence classification include the so-called Long-Short
Term Memory (LSTM) cells [36] and Gated Recurrent Units (GRUs) [37].
A single LSTM cell consists of three main gates: the input, output and the forget gate (see Figure 1).
Individual activations within a LSTM cell are defined as sigmoid functions:
σ(x) = 1
All three gates together form a feedback loop preserving gradients during the training. The main
benefit for sequence learning is that LSTMs to some extent solve the vanishing gradient problem,
i.e., long term signals remain in the memory, whereas a simple feedforward architecture is prone to
vanishing gradients.
ht - 1
Ct - 1
mul mul
Figure 1.
The LSTM cell. The forget gate is responsible for selective information filtering during the
learning step [
]. Here, the
corresponds to the memory state at learning step
1. We refer
the interested reader to [38] for a more detailed overview of the LSTM cells shown here.
One issue common to all neural network models is that they often overfit the data. One of the
most common solutions is the introduction of dropout layers [
] (at each training step, a percentage
of neurons is omitted from being trained). We use them for regularization.
To achieve the state-of-the-art performance, sets of trained neural networks can be combined
into neural ensembles. Some of the well known approaches which exploit this property include
HDLTex [
] and RMDL [
]. Both approaches focus on learning of different aspects of the data set,
yielding robust and powerful ensamble classification methods for e.g., text classification.
Large success of neural networks for classification is due to their capability of learning latent
relationships in the data. In this work, we evaluate how additional information in the form of
taxonomies affects the learning process. Even though feature engineering is becoming less relevant
in the era of deep learning [
], we believe that integrating background knowledge can potentially
improve classification models, especially when data is scarce, which is one of the currently unsolved
problems related to deep architectures.
3. Proposed SRNA Approach
This section presents the proposed SRNA (Semantics-aware Recurrent Neural Architecture)
approach, which leverages knowledge from taxomomies for construction of novel features for use in a
custom deep neural network architecture. Figure 2outlines the proposed two-step approach. In step 1
(described in Section 3.1), an input corpus
and a hypernym taxonomy are used to construct separate
feature matrices
. In step 2 (described in Section 3.2), the two matrices are input into a hybrid
neural network architecture to predict labels of new input documents.
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Hypernym taxonomy (WordNet)
Figure 2.
Visualization of the SRNA approach to semantic space propositionalization and learning.
Left: A document corpus
and a hypernym taxonomy (WordNet). Middle: A matrix of word indexes
obtained from corpus
, and a matrix of semantic features vectors
(with the same number of
rows as
), with features obtained from different levels of the taxonomy. Right: A hybrid neural
network architecture is learned from the word index vectors and the semantic feature vectors. Note that
sequential word information is present only in the vectors constituting matrix
(word indices), hence
part of the architecture exploits sequential information, whereas the constructed semantic features are
input to the dense feedforward part of the architecture. Prior to the final layer, intermediary layers of
both parts of the network are merged.
3.1. Propositionalization of the Semantic Space
The first step of the SRNA approach is hypernym identification and selection. We investigate how
hypernyms can be used as additional background knowledge to possibly improve the classification.
We rely on WordNet [
], a large and widely used lexical resource, in which words are annotated with
word senses (i.e., word meanings) and connected by semantic relations, including synonymy (e.g.,
car auto
), hypernymy (e.g.,
car vehicle
) and hyponymy (e.g.,
vehicle car
). In this work, we
explore only the space of hypernymy relations. The obtained hierarchical structure is thus a taxonomy.
In order to leverage the extensive knowledge stored in word taxonomies, a propositionalization
algorithm was developed, performing the fusion of the original set of documents
, represented by
word index matrix
of dimension
( is the user defined parameter for determining the dimension
of their feature vectors, corresponding to the number of word indices used), with newly constructed
semantic features. These features are the hypernyms, forming the columns of the semantic feature
of dimension
. The process of propositionalization merges (concatenates) the original
matrix Dand the sematic feature matrix Sinto novel matrix DS of dimension N×(`+m).
The semantic feature matrix
is constructed as follows. First, the corpus is processed document
by document. For each document
, we collect the words appearing in
and, for every word
, we
store the number of times it appears (its “frequency”). Next, for every
, we obtain the set of its
representative hypernyms. We make no attempt at word-sense disambiguation and leave this aspect for
further work. Instead, for words with several corresponding synsets (words with multiple senses), a
is representative if it is a hypernym for every sense of the word
, by which we avoid the
fact that we are missing information on the actual sense of the word in context. Thus, we identify the
set of all corresponding WordNet synsets of
(denoted by
), and the representative hypernyms of
word w, denoted by Aw, are hypernyms of all the synonyms in Sw:
{h|his a hypernym of s}.
We also store “frequencies” of all representative hypernym counts—for a hypernym
, the
frequency of
is defined as the sum of the frequencies of all of its hyponyms. Note that more general
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hypernyms will occur more often, hence the hierarchical relations between hypernyms are captured
via hypernym frequency.
Once representative hypernyms are identified for all words appearing in a document
, the set
is constructed as
, and, once this set is constructed for all documents, the set
constructed as the set of all representative hypernyms of the corpus, i.e.,
H=Sd∈D Hd
. Throughout
this process, counts of hypernym occurences are stored for each document, and once all documents are
processed, features are constructed based on the overall hypernym counts. The number of semantic
feature vectors to be constructed, denoted
, is a parameter of the proposed algorithm. The upper
bound for
, i.e., the number of all representative hypernyms. We propose three approaches,
which prioritize the hypernyms according to their frequency of occurrence. The three approaches used
to select λhypernyms for semantic feature vector construction are:
top λmost frequent terms,
last λterms (very rare terms),
a set of random λterms.
The obtained matrix can be used for learning either as a separate semantic feature set (S) or as the
whole DS matrix along with word-index matrix D.
3.2. Learning from the Semantic Space
The second step of the SRNA approach consists of training a deep architecture using the expanded
feature matrix (
) obtained in the first step. In SRNA, semantic features are fed into a deep
architecture along with document vectors. The outline of the architecture, shown in Figure 2, can be
represented in three main parts. The first part is responsible for learning from document vectors, and
is denoted by
. The second part learns from the constructed semantic vectors, denoted as
. Finally,
before output layer, outputs of
are merged and processed jointly. We denote this part by
(D+S). We give exact (hyperparameter) parameterization of the architecture in Section 4.
The recurrent part of the network, represented by the
part, is in this work defined as follows.
An input vector of word indices is first fed into an embedding layer with dropout regularization. The
resulting output is used in a standard LSTM layer. The output of this step is activated by a ReLU
activation function, defined as:
ReLU(x) = max(0, x).
The output of this layer is followed by a MaxPooling layer. Here, maximal values of a kernel
moving across the input vector are extracted. Finally, a dense layer with dropout regularization is
used. Formally, the Dpart of the network can be defined as:
L(1)=Dro pout(Emb(D)),
L(3w)=Dro pout(WT
part of the architecture similarly consists of fully connected layers. The input for this part
of the network are generated semantic features S. It can be represented as:
L(2)=Dro pout(L(1)),
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Here, we use the exponential linear unit [43], defined as
Elu(x) = (x, for x0,
c(ex1), for x<0.
is a constant determined during parameterization of the architecture. Outputs of
parts of the architecture are concatenated and used as input to a set of fully connected (dense) layers
(M), defined as:
L(2)=Elu(Dro pout(WT
operator merges the outputs of the two individual parts of the network into a single
matrix. For concatenation, one of the dimensions (in our case,
, the number of instances) of the two
output layers must be the same.
Finally, the output layer
includes one neuron for each class in the data set. We use binary
cross entropy as the loss function. The exact layer parameterizations are discussed in the experimental
setting section. The Adam optimizer [
] was chosen due to faster convergence. Formulation of the
whole SRNA approach is presented in Algorithm 1.
Algorithm 1 Semantic space propositionalization with learning.
1: Data: corpus D,WordNet taxonomy
2: for all document in Ddo
3: for all word in document do
4: Find hypernyms (based on WordNet) for word, store them and their counts
5: end for
6: Compute intersection of hypernym paths
7: end for
8: Assign feature values based on hypernym frequency in a document
9: S:= Select top λhypernyms as features based on overall hypernym frequency
10: D:= transform Dinto a matrix of word indices Learn a deep model using matrices Dand S.
The proposed algorithm’s temporal complexity is linear with respect to document number, making
it scalable even for larger corpora. Similarly, the frequency count estimation is not computationally
expensive. One of the key goals of this work was to explore how semantic information, derived
from individual documents, affects the learner’s performance. The SRNA code is accessible at https:
In the next section, we continue with the experimental setting where we evaluate the proposed
4. Experimental Setting
We compared the performance of the SRNA approach against multiple baseline classifiers. We
tested the methods on three benchmark data sets. We next describe the experimental setting in
more detail.
4.1. Data Sets
All documents were padded to the maximum dimension of 150 words. We conduct a series
of experiments, where we truncate the training documents (
) to lengths from 15 to 150 by the
increment of 10. The semantic feature matrix
is constructed using truncated documents. Note that
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the number of documents remains the same; we only experiment with the number of words per
document. The results were obtained using 10 fold stratified cross validation. We tested the proposed
approach on three data sets, listed below.
Reuters data set
consists of 11,263 newspaper articles, belonging to 46 different topics (classes). This
data set is loaded via the Keras library, where it is also publicly accessible (
IMDB review data set
consists of 50,000 reviews. Here, the goal is to predict the sentiment of
individual reviews (positive or negative). The data set was obtained from the Keras library [
where it is also accessible.
PAN reviews data set
consists of reviews written by 4160 authors (2080 male and 2080 female).
Reviews written by the same author are concatenated in a single document. The goal is to
classify the author’s gender. Detailed description of the data set is given in [10].
4.2. Semantic Feature Construction
We generated 1000 semantic features for each of the feature selection approaches. After initial
tests, we observed that the sparse feature set (rarest hypernyms) outperforms the other two approaches,
thus this setting was used for further tests. To reduce the number of candidate hypernym features,
we introduce a minimum frequency threshold—a threshold above which we consider a hypernym as
a potential feature. The frequency threshold used was 10, i.e., a hypernym is common to at least 10
words from the corpus in order to be considered for feature construction. (Note that this step of the
approach could be possibly improved using e.g., the RelieF) [46] branch of algorithms.
4.3. Deep Neural Architectures Used
As part of experimental evaluation, we test three deep learning models, two with inclusion of
semantic vectors and a baseline ConvNet. All the models are initiated in the same way.
SRNA: Recurrent architecture.
This is the proposed architecture that we described in Section 3. It
learns by using LSTM cells on the sequential word indices, and simultaneously captures semantic
meaning using dense layers over the semantic feature space.
Baseline RNN.
The baseline RNN architecture consists of the non-semantic part of SRNA. Here, a
simple unidirectional RNN is trained directly on the input texts.
Baseline CNN.
The baseline neural networks used are a 1D convolutional neural network and a
recurrent neural network with the same architecture as SRNA, where we omit the semantic part.
Here, only word index vectors are used as inputs. The network was parameterized as follows.
The number of filters was set to 64, the kernel size used was 5. The MaxPooling region was of
size 5. The outputs of the pooling region were used as input to a dense layer with 48 neurons,
followed by the final layer.
One of the main problems with small data sets and neural networks is overfitting. Each neural
network is trained incrementally, where the training is stopped as soon as the network’s performance
starts to degrade. Furthermore, dropout layers are used for additional regularization (the dropout rate
was set to 0.5). The alpha parameter of each Elu activation function was set to 1.
As an additional baseline, we implemented also two non-neural classifiers, i.e., the random forest
classifier, and a support vector machine, where we also tested how semantic vectors contribute to
classification accuracy.
The random forest (RF)
classifier was initialized as follows: number of trees for classification from
documents was set to the average document length present in a given corpus rounded to the
closest integer. One versus all (OVA) classification scheme was used for the multi-class Reuters
task. To evaluate the semantic addition, we implemented two variants of random forests, both
learned from identical input as given to neural networks.
Semantic RF
is the random forest that
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leverages semantic information (i.e.,
matrix), while
is trained exclusively on TF-IDF
word vectors obtained from D.
Support vector machine (SVM)
classifier [
] was trained as follows. We used the RBF kernel and
the C value determined over a grid search over range [0.1,1,10]. Similarly to random forests,
we also implemented the version called
Semantic SVM
, which uses SRNA’s semantic features
along with TF-iDF matrix as input.
Other Technical Details
The SRNA approach was along with Baseline RNN and CNN architectures implemented in
Keras framework, where we used the Tensorflow computational back-end [
]. The other classifiers
were called from the Scikit-learn Python library [
]. All approaches were tested on a Nvidia Titan
GPU (NVIDIA, Santa Clara, CA, USA) . The baseline Random Forest classifier was implemented in
Scikit-learn [
]. Matrix-based operations in the propositionalization step used the Numpy library [
5. Results and Discussion
For all data sets, we measure the accuracy. In case of Reuters, which is a multiclass problem,
the exact accuracy is also termed subset accuracy (or exact match ratio). We also compute the F1
score for the IMDB and PAN data sets, and micro F1 for Reuters. Each experiment with 10 fold cross
validation is repeated five times, and the results are averaged. To statistically evaluate the results, we
used the Friedman’s test, followed by the Nemenyi post hoc correction. The results are presented
according to the classifier’s average ranks along a horizontal line [
]. The obtained critical distance
diagrams are interpreted as follows: if one or more classifiers are connected with a bold line, their
performance does not differ significantly (at alpha = 0.05). We rank the classifiers for each data set,
for each individual subsample. Furthermore, we visualize the performance of SRNA compared to
baseline RNN using the recently introduced Bayesian hierarchical
-test—a Bayesian alternative to
pairwise classifier comparison over multiple data sets [
]. Here, instead of significance level, a rope
parameter is set. This parameter determines the threshold, under which we consider the difference
in classifier performance to be the same. In this work, we set this threshold to 0.01. Note that the
hierarchical Bayesian
-test offers the opportunity to explore the pairwise comparison of classifiers in
more detail, hence we use it to inspect the SRNA vs. Baseline RNN combination.
For different document lengths, we calculate the accuracy and F1 scores, for which the plots (for
the sequence length up to 100) are provided in Figures 3and 4, respectively. It can be seen that, on the
Reuters data set, SRNA outperforms other approaches in terms of Accuracy and F1, while for the other
two data sets it achieves comparable results to baseline RNN and CNN.
Figure 3. Accuracy results on three benchmark data sets.
We also present critical distance diagrams for the accuracy (Figure 5) and F1 measures (Figure 6).
From the ranks, we can see that the SRNA approach outperforms all other baselines. However, the
differences in performance between the SRNA approach and Baseline RNNs (as well as most of other
classifiers) are not significant, and are data set dependent.
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Figure 4. F1 results on three benchmark data sets.
Figure 5. Accuracy—CD diagram.
Figure 6. (Micro) F1—CD diagram.
Interestingly, the semantic feature-augmented random forests on average outperform their basic
counterparts. This observation indicates that the semantic features could be used in a general
classification setting, where an arbitrary classifier could benefit from the background knowledge
introduced. Rigorous, large-scale experimental proof of this statement is out of the scope of this study.
As the goal of the proposed SRNA approach is to improve learning on very small data sets, with
very limited data, we further investigate the classifier’s performance on up to 100 words (see Figures 3
and 4).
When the considered recurrent architectures were inspected in more detail (Figure 7), we can
observe that there is a higher probability that SRNA outperforms (Prob = 0.64) the baseline RNNs
(Prob = 0.30), when the region of practical equivalence (ROPE) is set to 0.01, even though the
performances of the two architectures are very similar. As an input to this test, we used differences in
classifiers’ performances from five repetitions of 10 fold cross validation.
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Mach. Learn. Knowl. Extr. 2019,xx, 5 11 of 15
Figure 7.
Sampled probability density of differences in classifier performance. Overall, the SRNA
approach outperforms the baseline RNN, yet the larger differences in performance (e.g., Reuters data
set) are data set-dependent. Higher probability of winning (0.64) in favour of SRNA indicates that
semantic features potentially improve performance. Note that the ROPE parameter was for this test set
to 0.01.
We further investigate the reasons why the baseline convolutional network performs very poorly
when only up to 50 words are used. We believe the poor performance is related to the small data size.
The CNN learns normally on very reduced documents, yet when its predictions were inspected, we
observed it was not able to produce a single positive classification.
This behaviour was observed for document length
50, which resulted in two valid classifications
length =
50), whereas all other classifications (
length <
50) returned
0% accuracy. The difference in
accuracy for very short document lengths serves as an additional empirical proof that semantic vectors
can at least augment the signal up to the classification threshold when using the SRNA.
The SVM approaches do not perform well in the conducted experiments. We believe the
reason for this could lie in too small grid search region, as well as the noise potential introduced
by semantic features. This indicates that the semantic features could be further pruned—such noise
can have observable effects on the network’s performance when semantic vectors are merged with the
word vectors.
We observe that the SVM classifier did not perform well, when semantic features were added.
Even though we did not test the regularization (C) range exhaustively, we believe that the SVMs’
performance could be further improved. Moreover, the RBF kernel is not necessarily the optimal
kernel choice.
Furthermore, we discuss the performance of random forests. The random forest classifier is in the
majority of settings outperformed by other approaches (apart from SVMs), which is not surprising as
very simple forest construction was used. However, we can see that with random forests the use of
semantic features provides improvement. As compared to SVMs, random forests use a relatively low
number of features; it is therefore easier to observe a difference in performance when novel features
are introduced.
Interestingly, the random forest’s performance appears to degrade in the case of the Reuters
data set, which could indicate overfitting. As we used an OVA classification scheme, this decline in
performance could be possibly solved by more advanced multi-class approaches, such as some form
of predictive clustering trees. It is also possible that the problem is simply too hard for a random
forest classifier used in this study, as it was not able to recognize any meaningful pattern, useful for
classification into one of the possible topics.
Figure 7.
Sampled probability density of differences in classifier performance. Overall, the SRNA
approach outperforms the baseline RNN, yet the larger differences in performance (e.g., Reuters data
set) are data set-dependent. Higher probability of winning (0.64) in favour of SRNA indicates that
semantic features potentially improve performance. Note that the ROPE parameter was for this test set
to 0.01.
We further investigate the reasons why the baseline convolutional network performs very poorly
when only up to 50 words are used. We believe the poor performance is related to the small data size.
The CNN learns normally on very reduced documents, yet when its predictions were inspected, we
observed it was not able to produce a single positive classification.
This behaviour was observed for document length
50, which resulted in two valid classifications
length =
50), whereas all other classifications (
length <
50) returned
0% accuracy. The difference in
accuracy for very short document lengths serves as an additional empirical proof that semantic vectors
can at least augment the signal up to the classification threshold when using the SRNA.
The SVM approaches do not perform well in the conducted experiments. We believe the
reason for this could lie in too small grid search region, as well as the noise potential introduced
by semantic features. This indicates that the semantic features could be further pruned—such noise
can have observable effects on the network’s performance when semantic vectors are merged with the
word vectors.
In addition,we observe that the SVM classifier did not perform well also when semantic features
were added. Even though we did not test the regularization (C) range exhaustively, we believe that
the SVMs’ performance could be further improved. Moreover, the RBF kernel is not necessarily the
optimal kernel choice.
Furthermore, we discuss the performance of random forests. The random forest classifier is in the
majority of settings outperformed by other approaches (apart from SVMs), which is not surprising as
very simple forest construction was used. However, we can see that with random forests the use of
semantic features provides improvement. As compared to SVMs, random forests use a relatively low
number of features; it is therefore easier to observe a difference in performance when novel features
are introduced.
Interestingly, the random forest’s performance appears to degrade in the case of the Reuters
data set, which could indicate overfitting. As we used an OVA classification scheme, this decline in
performance could be possibly solved by more advanced multi-class approaches, such as some form
of predictive clustering trees. It is also possible that the problem is simply too hard for a random
forest classifier used in this study, as it was not able to recognize any meaningful pattern, useful for
classification into one of the possible topics.
Even though this study is not devoted to improving the overall state-of-the-art classification
performance (SOTA), but to demonstrate how semantic features contribute to their semantically
Mach. Learn. Knowl. Extr. 2019,1586
unaware counterparts, and especially how semantic features can be introduced in the neural
architectures, we briefly discuss here SOTA results.
Currently, the best accuracy for the IMDB data set is estimated at around 98% for an approach that
is based on paragraph vectors [
]. The authors compared their approach also with simple LSTMs (as
used for baseline in this study), and obtained accuracies of 96%. We tested our baseline on the whole
data set, and it performed similarly (95.3%), which serves as a validation of the baseline approach
used in this study. Next, the accuracy on the Reuters data set was recently reported to be 80–85%,
where multi-objective label encoders were used [
]. Our baseline implementation performs with 75%
accuracy. Finally, SOTA for gender classification on PAN 2014 was reported to be around 73% [10].
Even if we investigated a particular aspect of text classification, not directly associated with SOTA,
we will try to perform a more systematic evaluation to SOTA approaches in future work, however
there are some limitations, such as computational cost of training very large networks and the fact
that the majority of SOTA approaches do not account for a situation with sparse data. However, we
believe that the proposed approach can be adapted to make current SOTA architectures more robust,
especially when only fragments of inputs are considered.
6. Conclusions and Further Work
We developed an approach for propositionalization of semantic space in the form of taxonomies
to improve text classification tasks. We explore possible deep architectures, which learn separately
from the two feature spaces and prove that construction of such architectures can significantly improve
overall classification on short document fragments. As we tested only three simple approaches for
feature selection, this work could further benefit from more advanced feature selection techniques,
such as the ones based on evolutionary computation or ReliefF branch of algorithms. We believe a
more sophisticated feature selection approach would result in more relevant features, and could as
such significantly speed up the learning phase. Furthermore, the approach could be tested in a setting
where no feature selection is performed at all—for such experiments, one would need significantly
more performant GPUs than the ones used in this experiment. We believe the neural networks would
be able to select relevant features in an end-to-end manner.
As the results in this study indicate, recurrent neural architecture can indeed benefit from addition
of semantic information, and part of the further work includes more extensive experimental tests,
where state-of-the-art approaches, such as RMDL, HDLTex or hierarchical attention networks shall be
combined with the proposed hypernym features.
As current state-of-the-art text classification approaches also work on the character level, it is
yet to be investigated whether the proposed approach can also boost performance for character level
architectures. Furthermore, the SRNA approach could potentially benefit from different types of
recurrent layers, such as, for example, gated recurrent units (GRUs).
Last but not least, in a higher performance setting, the effects of semantic features could be
evaluated on current SOTA algorithms, as well as on inherently short texts, such as tweets and
comments. We will also include comparison of the proposed approach of semantic knowledge
integration to enrichment with precomputed word embeddings.
Author Contributions:
conceptualization, B.Š., S.P. and J.K.; methodology, B.Š., J.K.; software, B.Š.; validation,
B.Š., J.K., N.L. and S.P.; formal analysis, J.K.; investigation, S.P., B.Š.; resources, B.Š., S.P.; data curation, B.Š.;
writing—original draft preparation, all authors; writing—review and editing, all authors; visualization, B.Š.;
supervision, N.L., S.P.; project administration, S.P., N.L.; funding acquisition, S.P., N.L.
The work of the first author was funded by the Slovenian Research Agency through a young researcher
grant. The work of other authors was supported by the Slovenian Research Agency (ARRS) core research
programme Knowledge Technologies (P2-0103) and ARRS funded research project Semantic Data Mining for Linked
Open Data (financed under the ERC Complementary Scheme, N2-0078). This paper is supported also by the
European Union’s Horizon 2020 research and innovation programme under Grant No. 825153, EMBEDDIA
(Cross-Lingual Embeddings for Less-Represented Languages in European News Media). The results of this
Mach. Learn. Knowl. Extr. 2019,1587
publication reflect only the authors’ views and the Commission is not responsible for any use that may be made of
the information it contains.
Acknowledgments: The GPU used for this research was donated by the NVIDIA Corporation.
Conflicts of Interest: The authors declare no conflict of interest.
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... Many approaches have been deployed for performing it. Traditionally, the approaches that can be found in literature for text classification include naive Bayes classifier, k-nearest neighbors, artificial neural network, evolutionary approaches, support vector machines, decision trees etc [8]- [11]. The training of the classifier can be either feature based or end-to-end learning without the need of the step of feature extraction. ...
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... One of the challenges in the classic machine learning algorithms relies on the several preprocessing steps, such as tumour segmentation, which regularly requires manual correction of the tumour boundaries computed by the (semi)automated algorithms which increases cost, time and the risk of inter-observer variation [92]. Deep learning can automatically perform these steps on the raw data and is considered a powerful analytical tool for different predictive data mining applications, especially in complex processes like biological systems [93][94][95][96]. Machine and deep learning-based artificial intelligence (AI) imaging techniques have the potential to support the decision making in clinical oncology for precise imaging. ...
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... The semantic text-mining approach is significant for text classification. Škrlj et al. [22] presented a practical semantic content-mining approach, which changes semantic data identified from a given set of documents into many novel highlights used for learning. Their proposed semantics-aware recurrent neural architecture (SRNA) empowers the system to obtain semantic vectors and raw text documents at the same time. ...
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Unstructured data from the internet constitute large sources of information, which need to be formatted in a user-friendly way. This research develops a model that classifies unstructured data from data mining into labeled data, and builds an informational and decision-making support system (DMSS). We often have assortments of information collected by mining data from various sources, where the key challenge is to extract valuable information. We observe substantial classification accuracy enhancement for our datasets with both machine learning and deep learning algorithms. The highest classification accuracy (99% in training, 96% in testing) was achieved from a Covid corpus which is processed by using a long short-term memory (LSTM). Furthermore, we conducted tests on large datasets relevant to the Disaster corpus, with an LSTM classification accuracy of 98%. In addition, random forest (RF), a machine learning algorithm, provides a reasonable 84% accuracy. This research’s main objective is to increase the application’s robustness by integrating intelligence into the developed DMSS, which provides insight into the user’s intent, despite dealing with a noisy dataset. Our designed model selects the random forest and stochastic gradient descent (SGD) algorithms’ F1 score, where the RF method outperforms by improving accuracy by 2% (to 83% from 81%) compared with a conventional method.
Different institutions have shown interest in standardizing the learning result. It may be used in the same way to assess students’ learning status. The teacher must quantify the learning outcomes for evaluation purposes. It often requires a great deal of time and effort to do paper tasks. Additionally, this activity prevents instructors from concentrating on the learning process. Teachers are continuously burdened with administrative responsibilities that should be alleviated using technology that adheres to the current framework. The Bloom Taxonomy, a widely used framework for defining learning outcomes, allows for the assessment of learning outcomes at several levels. The purpose of this research is to provide a framework that will assist the instructor in completing the evaluation more quickly and accurately. This study provided an algorithm for adapting ontology and text classification technologies to detect correlations between words and keywords to aid in evaluation. It is anticipated that the categorization findings will assist in shortening the time required to complete the evaluation.
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Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients’ overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.
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Modern data mining algorithms frequently need to address the task of learning from heterogeneous data, including various sources of background knowledge. A data mining task where ontologies are used as background knowledge in data analysis is referred to as semantic data mining. A specific semantic data mining task is semantic subgroup discovery: a rule learning approach enabling ontology terms to be used in subgroup descriptions learned from class labeled data. This paper presents Community-Based Semantic Subgroup Discovery (CBSSD), a novel approach that advances ontology-based subgroup identification by exploiting the structural properties of induced complex networks related to the studied phenomenon. Following the idea of multi-view learning, using different sources of information to obtain better models, the CBSSD approach can leverage different types of nodes of the induced complex network, simultaneously using information from multiple levels of a biological system. The approach was tested on ten data sets consisting of genes related to complex diseases, as well as core metabolic processes. The experimental results demonstrate that the CBSSD approach is scalable, applicable to large complex networks, and that it can be used to identify significant combinations of terms, which can not be uncovered by contemporary term enrichment analysis approaches.
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Conference Paper
The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can accept as input a variety data to include text, video, images, and symbolic. This paper describes RMDL and shows test results for image and text data including MNIST, CIFAR-10, WOS, Reuters, IMDB, and 20newsgroup. These test results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems.
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The task of extracting the used feature vector in mining tasks (classification, clustering …etc.) is considered the most important task for enhancing the text processing capabilities. This paper proposes a novel approach to be used in building the feature vector used in web text document classification process; adding semantics in the generated feature vector. This approach is based on utilizing the benefit of the hierarchal structure of the WordNet ontology, to eliminate meaningless words from the generated feature vector that has no semantic relation with any of WordNet lexical categories; this leads to the reduction of the feature vector size without losing information on the text, also enriching the feature vector by concatenating each word with its corresponding WordNet lexical category. For mining tasks, the Vector Space Model (VSM) is used to represent text documents and the Term Frequency Inverse Document Frequency (TFIDF) is used as a term weighting technique. The proposed ontology based approach was evaluated against the Principal component analysis (PCA) approach, and against an ontology based reduction technique without the process of adding semantics to the generated feature vector using several experiments with five different classifiers (SVM, JRIP, J48, Naive-Bayes, and kNN). The experimental results reveal the effectiveness of the authors' proposed approach against other traditional approaches to achieve a better classification accuracy F-measure, precision, and recall.
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Conference Paper
The continually increasing number of documents produced each year necessitates ever improving information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an important application for supervised learning. Recently the performance of these traditional classifiers has degraded as the number of documents has increased. This is because along with this growth in the number of documents has come an increase in the number of categories. This paper approaches this problem differently from current document classification methods that view the problem as multi-class classification. Instead we perform hierarchical classification using an approach we call Hierarchical Deep Learning for Text classification (HDLTex). HDLTex employs stacks of deep learning architectures to provide specialized understanding at each level of the document hierarchy.
Previous image classification approaches mostly neglect semantics, which has two major limitations. First, categories are simply treated independently while in fact they have semantic overlaps. For example, “sedan” is a specific kind of “car”. Therefore, it’s unreasonable to train a classifier to distinguish between “sedan” and “car”. Second, image feature representations used for classifying different categories are the same. However, the human perception system is believed to use different features for different objects. In this paper, we leverage semantic ontologies to solve the aforementioned problems. The authors propose an ontological random forest algorithm where the splitting of decision trees are determined by semantic relations among categories. Then hierarchical features are automatically learned by multiple-instance learning to capture visual dissimilarities at different concept levels. Their approach is tested on two image classification datasets. Experimental results demonstrate that their approach not only outperforms state-of-the-art results but also identifies semantic visual features.
Multi-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains. However, most previous works treat labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential information and makes it difficult for these models to jointly learn three or more tasks. In this paper, we propose Multi-Task Label Embedding to convert labels in text classification into semantic vectors, thereby turning the original tasks into vector matching tasks. We implement unsupervised, supervised and semi-supervised models of Multi-Task Label Embedding, all utilizing semantic correlations among tasks and making it particularly convenient to scale and transfer as more tasks are involved. Extensive experiments on five benchmark datasets for text classification show that our models can effectively improve performances of related tasks with semantic representations of labels and additional information from each other.
Conference Paper
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.
Technical Report
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively.
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Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates $backslash$emphdeep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.
Conference Paper
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implemen- tation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called dropout that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry