Wenzhi Huang's research while affiliated with Wuhan Institute of Technology and other places
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Publications (7)
The task of event extraction consists of three subtasks namely entity recognition, trigger identification and argument role classification. Recent work tackles these subtasks jointly with the method of multi-task learning for better extraction performance. Despite being effective, existing attempts typically treat labels of event subtasks as uninfo...
Named entity recognition (NER) is one fundamental task in natural language processing, which is usually viewed as a sequence labeling problem and typically addressed by neural conditional random field (CRF) models, such as BiLSTM-CRF. Intuitively, the entity types contain rich semantic information and the entity type sequence in a sentence can glob...
Extracting events from texts using neural networks has gained increasing research focus in recent years. However, existing methods prepare candidate arguments in a separate classifier suffering from the error propagation problem and fail to model correlations between entity mentions and event structures. To improve the performance of both entity re...
[This corrects the article DOI: 10.1371/journal.pone.0235796.].
Chinese information extraction is traditionally performed in the process of word segmentation, entity recognition, relation extraction and event detection. This pipelined approach suffers from two limitations: 1) It is prone to introduce propagated errors from upstream tasks to subsequent applications; 2) Mutual benefits of cross-task dependencies...
Convolutional Neural Network (CNN) have been successfully used for many natural language processing applications. In this paper, we propose a novel CNN model for sentence-level paraphrase identification. We learn the sentence representations using character-aware convolutional neural network that relies on character-level input and gives sentence-l...
Most phrase embedding methods consider a phrase as a basic term and learn embeddings according to phrases’ external contexts, ignoring the internal structures of words and characters. There are some languages such as Chinese, a phrase is usually composed of several words or characters and contains rich internal information. The semantic meaning of...
Citations
... Deep learning models are widely employed in natural language processing [1,2], image recognition [3], etc. In addition, the performance of deep learning models depends on the number of annotated datasets [4], especially in specific fields, deep learning models are more dependent on annotated datasets. ...
... In recent years, EE has received growing attention in many domains, such as Finance, Public Safety, Intelligent Operations, and Maintenance, because it can produce valuable structured event knowledge to facilitate critical incident handling in these domains. Most existing approaches [1][2][3][4][5][6] mainly explore sentence-level EE (SEE), which detects and extracts events from a single sentence within the given document. Moreover, the evaluation work of these approaches is mainly based on a manually annotated benchmark, ACE-2005 [7], which labels only event arguments within a sentence scope. ...
... Previously, we have presented a transition-based method [27] that approaches the joint learning in a left-to-right decoding order, which has been proven to be better than simple shared-private models. However, it suffers from two limitations: 1) The elaborate modification to the standard LSTM hinders the computation of multiple sentence in a batch and not all lexicons that related to a character are used; 2) The interactive semantics of all task labels have not been fully explored, in the sense that the event label information has not been introduced into the shared representations. ...
... Moreover, learning algorithms avoid the feature extraction procedure due to directly considering input to the network. Another advantage of convolution is, it helps to learn semantic information of the text documents and also minimize the impact of ambiguous terms [21,22]. The basic idea of convolution is singlehand sliding window concept, which splits text documents into flexible phrases. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/838167242473474-1576846051971_Q64/Jiangping-Huang.jpg)
... In recent years, many researchers have realized that only by considering both the internal and external information of a phrase can a good phrase embedding be obtained. Huang et al. [13] first proposed that pre-trained phrase embeddings and component word embeddings were added and spliced to obtain new phrase embeddings. This method is simple and easy to implement, but the effect is limited. ...
