Donghong Ji's research while affiliated with Wuhan University and other places
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Publications (211)
The rapid development of aspect-based sentiment analysis (ABSA) within recent decades shows great potential for real-world society. The current ABSA works, however, are mostly limited to the scenario of a single text piece, leaving the study in dialogue contexts unexplored. In this work, we introduce a novel task of conversational aspect-based sent...
So far, discontinuous named entity recognition (NER) has received increasing research attention and many related methods have surged such as hypergraph-based methods, span-based methods, and sequence-to-sequence (Seq2Seq) methods, etc. However, these methods more or less suffer from some problems such as decoding ambiguity and efficiency, which lim...
We investigate distantly supervised relation extraction with knowledge-guided latent graphs and an iterative graph learner. For the relation extraction tasks, we assume that the input sentences contain latent graphs with useful structural information between mentions and relations of the distantly supervised data. We first embed input sentences wit...
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Relation Extraction (RE) is a fundamental task of information extraction, which has attracted a large amount of research attention. Previous studies focus on extracting the relations within a sentence or document, while currently researchers begin to explore cross-document RE. However, current cross-document RE methods directly utilize text snippet...
Conversational semantic role labeling (CSRL) is a newly proposed task that uncovers the shallow semantic structures in a dialogue text. Unfortunately several important characteristics of the CSRL task have been overlooked by the existing works, such as the structural information integration, near-neighbor influence. In this work, we investigate the...
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...
Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities towards certain aspects of products or services behind the social media texts or reviews, which has been a fundamental application to the real-world society. Within recent decade, ABSA has achieved extraordinarily high accuracy with various deep...
Emotion cause pair extraction (ECPE), as one of the derived subtasks of emotion cause analysis (ECA), shares rich inter-related features with emotion extraction (EE) and cause extraction (CE). Therefore EE and CE are frequently utilized as auxiliary tasks for better feature learning, modeled via multi-task learning (MTL) framework by prior works to...
Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text. Most prior work focuses on extracting flat events while neglecting overlapped or nested ones. A few models for overlapped and nested EE includes several successive stages to extract event triggers and argu...
Multi-label emotion prediction, which aims to predict emotion labels from text, attracts increasing attention recently. It is ubiquitous that emotion labels are highly correlated in this task. Existing state-of-the-art models solve multi-label emotion prediction in sequence-to-sequence (Seq2Seq) manner, while such label correlations are merely leve...
This paper studies the methodology of inferring bullish or bearish sentiments in the financial domain. The task aims to predict a real value to represent the sentiment intensity concerning a target (company or stock symbol) in a text. Previous researches have proved the validity of using deep neural networks to automatically learn semantic and synt...
Structured sentiment analysis is a newly proposed task, which aims to summarize the overall sentiment and opinion status on given texts, i.e., the opinion expression, the sentiment polarity of the opinion, the holder of the opinion, and the target the opinion towards. In this work, we investigate a transition-based model for end-to-end structured s...
Recent research attention for relation extraction has been paid to the dialogue scenario, i.e., dialogue-level relation extraction (DiaRE). Existing DiaRE methods either simply concatenate the utterances in a dialogue into a long piece of text, or employ naive words, sentences or entities to build dialogue graphs, while the structural characteristi...
Conversational semantic role labeling (CSRL) is a newly proposed task that uncovers the shallow semantic structures in a dialogue text. Unfortunately several important characteristics of the CSRL task have been overlooked by the existing works, such as the structural information integration, near-neighbor influence. In this work, we investigate the...
So far, aspect-based sentiment analysis (ABSA) has involved with total seven subtasks, in which, however the interactions among them have been left unexplored sufficiently. This work presents a novel multiplex cascade framework for unified ABSA and maintaining such interactions. First, we model total seven subtasks as a hierarchical dependency in t...
So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied individually. Recently, a growing interest has been built for unified NER, tackling the above three jobs concurrently with one single model. Current best-performing methods...
Unified opinion role labeling (ORL) aims to detect all possible opinion structures of 'opinion-holder-target' in one shot, given a text. The existing transition-based unified method, unfortunately, is subject to longer opinion terms and fails to solve the term overlap issue. Current top performance has been achieved by employing the span-based grap...
Deceptive reviews detection has attracted extensive attentions from the business and research communities in recent years. Existing work mainly uses traditional discrete models with rich features from the viewpoint of linguistics and psycholinguistics. The drawback is that these models fail to capture the global semantic information of a sentence o...
The pair-wise aspect and opinion term extraction (PAOTE) task aims to extract aspect terms and opinion terms from reviews in the form of opinion pairs, which provides a global profile for reviews of goods or users. Up-to-date studies ignore the interaction between term detection and term pairing, which may be crucial for the PAOTE task. Other studi...
The state-of-the-art model for structured sentiment analysis casts the task as a dependency parsing problem, which has some limitations: (1) The label proportions for span prediction and span relation prediction are imbalanced. (2) The span lengths of sentiment tuple components may be very large in this task, which will further exacerbate the imbal...
So far, discontinuous named entity recognition (NER) has received increasing research attention and many related methods have surged such as hypergraph-based methods, span-based methods, and sequence-to-sequence (Seq2Seq) methods, etc. However, these methods more or less suffer from some problems such as decoding ambiguity and efficiency, which lim...
Biomedicalevent extraction is an essential task in the biomedical research. Existing models suffer from the issue of low recall due to the large proportion of unrecognized events and inflexible event argument combination. To address this issue, we propose an end-to-end multi-task approach for biomedical event extraction. Our model is able to achiev...
Aspect-based sentiment information extraction has attracted increasing attention in the research community of natural language processing. Various methods, such as sequence tagging, sequence-to-sequence generation and span-based extraction, have been proposed, which own different advantages and disadvantages. In this article, we revisit the span-ba...
So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied individually. Recently, a growing interest has been built for unified NER, tackling the above three jobs concurrently with one single model. Current best-performing methods...
Aspect-based sentiment triplet extraction (ASTE) aims at recognizing the joint triplets from texts, i.e., aspect terms, opinion expressions, and correlated sentiment polarities. As a newly proposed task, ASTE depicts the complete sentiment picture from different perspectives to better facilitate real-world applications. Unfortunately, several major...
Depression is a widespread and intractable problem in modern society, which may lead to suicide ideation and behavior. Analyzing depression or suicide based on the posts of social media such as Twitter or Reddit has achieved great progress in recent years. However, most work focuses on English social media and depression prediction is typically for...
The extraction of opinion target–word pairs from user reviews has received much attention recently, since it can provide essential information for fine-grained opinion analysis. However, according to our statistics on a large-scale dataset of Chinese reviews, about 60% reviews do not explicitly show opinion targets or words. To investigate this pro...
Unified opinion role labeling (ORL) aims to detect all possible opinion structures of `opinion-holder-target' in one shot, given a text. The existing transition-based unified method, unfortunately, is subject to longer opinion terms and fails to solve the term overlap issue. Current top performance has been achieved by employing the span-based grap...
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...
Code-switched emotion detection, a task analyzing the emotion in code-switched texts, has gain increasing research attention within recent years. Prior works utilize various neural models with sophisticated features to pursuit high performance of the task, while they still overlook some crucial characteristics of the code-switched texts. In this wo...
The aspect-based sentiment triplet extraction (ASTE), as a complete sentiment analysis task, aims to recognize the aspect term, the opinion expression, and the sentiment polarity in a sentence. Current state-of-the-art ASTE models employ a joint extracting scheme for better task improvements. However, how to better solve the triplet overlap issues...
In this paper, we propose to enhance the pair-wise aspect and opinion terms extraction (PAOTE) task by incorporating rich syntactic knowledge. We first build a syntax fusion encoder for encoding syntactic features, including a label-aware graph convolutional network (LAGCN) for modeling the dependency edges and labels, as well as the POS tags unifi...
Pair-wise aspect and opinion terms extraction (PAOTE), aiming at detecting the pair of the correlated aspect and opinion terms jointly, recently has drawn increasing research attention in the community of sentiment analysis and opinion mining. Recent works largely employ joint methods for the task, while they do not sufficiently incorporate the ext...
![Figure 2: The architecture of our model. The input is "The [mitral] 1...](publication/353065282/figure/fig1/AS:1043129729830913@1625712916539/The-architecture-of-our-model-The-input-is-The-mitral-1-valve-leaflets-1-are-mildly_Q320.jpg)
Research on overlapped and discontinuous named entity recognition (NER) has received increasing attention. The majority of previous work focuses on either overlapped or discontinuous entities. In this paper, we propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly. The model includes two major steps....
Relation extraction from dialogue text is an innovative task in natural language processing. In addition to the general characteristics of general relation extraction from news or scientific publication text, the task is of certain special features. For example, the context in dialogues frequently switches between speakers, and there exist rich pro...
Semantic role labeling (SRL) is one of the important tasks in natural language processing. Current end-to-end SRL, compared with traditional pipeline SRL, has achieved competitive performance via graph-based neural models. However, these are all first-order methods, where decisions for detecting predicate-argument pairs are made in isolation with l...
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A majority of research interests in irregular (e.g., nested or discontinuous) named entity recognition (NER) have been paid on nested entities, while discontinuous entities received limited attention. Existing work for discontinuous NER, however, either suffers from decoding ambiguity or predicting using token-level local features. In this work, we...
Currently the unified semantic role labeling (SRL) that achieves predicate identification and argument role labeling in an end-to-end manner has received growing interests. Recent works show that leveraging the syntax knowledge significantly enhances the SRL performances. In this paper, we investigate a novel unified SRL framework based on the sequ...
End-to-end semantic role labeling (SRL) has been received increasing interest. It performs the two subtasks of SRL: predicate identification and argument role labeling, jointly. Recent work is mostly focused on graph-based neural models, while the transition-based framework with neural networks which has been widely used in a number of closely-rela...
In this paper, we propose to enhance the pair-wise aspect and opinion terms extraction (PAOTE) task by incorporating rich syntactic knowledge. We first build a syntax fusion encoder for encoding syntactic features, including a label-aware graph convolutional network (LAGCN) for modeling the dependency edges and labels, as well as the POS tags unifi...
Generating emotional responses plays an important role in human–computer conversational system. Adopting emotional information to the generation process improves user’s satisfaction and makes the generated responses more human-like. Furthermore using fixed and unrelated emotion dictionary limits the overall performance of recent models. In order to...
[This corrects the article DOI: 10.1371/journal.pone.0235796.].
The recognition of textual entailment (RTE) as the main text understanding task is crucial to the application in biomedical and clinical field, however, the developing of which has been hindered, due to the scarcity of the data annotation. In this work, we propose a domain adaptation framework for the cross-domain clinical RTE. We first construct a...
Nowadays many neural networks have been widely employed for semantic parsing problems especially for Structured Query Language (SQL) parsing, which aims at transforming natural language sentences into SQL representations. Selecting proper table headers in SQL tasks is extremely important, and the main cause of performance drop is that attention mec...
Implicit sentiment analysis is a challenging task because the sentiment of a text is expressed in a connotative manner. To tackle this problem, we propose to use textual events as a knowledge source to enrich network representations. To consider task interactions, we present a novel lightweight joint learning paradigm that can pass task-related mes...
Attention has been shown highly effective for modeling sequences, capturing the more informative parts in learning a deep representation. However, recent studies show that the attention values do not always coincide with intuition in tasks, such as machine translation and sentiment classification. In this study, we consider using deep reinforcement...
Event causality identification is an important research task in natural language processing. Existing methods largely focus on identifying explicit causal relations, and give poor performance in implicit causalities, especially in the document level. In this paper, we formalize event causality identification as a graph-based edge prediction problem...
End-to-end semantic role labeling (SRL) has been received increasing interest. It performs the two subtasks of SRL: predicate identification and argument role labeling, jointly. Recent work is mostly focused on graph-based neural models, while the transition-based framework with neural networks which has been widely used in a number of closely-rela...
Multi-turn response selection is a major task in building intelligent dialogue systems. Most existing works focus on modeling the semantic relationship between the utterances and the candidate response with neural networks like RNNs and various attention mechanisms. In this paper, we study how to leverage the advantage of pre-trained language model...
Motivation
Entity relation extraction is one of the fundamental tasks in biomedical text mining, which is usually solved by the models from natural language processing (NLP). Compared with traditional pipeline methods, joint methods can avoid the error propagation from entity to relation, giving better performances. However, the existing joint mode...
Information extraction is one of the important tasks in the field of Natural Language Processing (NLP). Most of the existing methods focus on general texts and little attention is paid to information extraction in specialized domains such as legal texts. This paper explores the task of information extraction in the legal field, which aims to extrac...
Overlapping entity relation extraction has received extensive research attention in recent years. However, existing methods suffer from the limitation of long-distance dependencies between entities, and fail to extract the relations when the overlapping situation is relatively complex. This issue limits the performance of the task. In this paper, w...
Lexical chain consists of cohesion words in a document, which implies underlying structure of a text, and thus facilitates downstream NLP tasks. Nevertheless, existing work focuses on detecting the simple surface lexicons with shallow syntax associations, ignoring the semantic-aware lexical compounds as well as the latent semantic frames, (e.g., to...
Aggressive language detection (ALD), detecting the abusive and offensive language in texts, is one of the crucial applications in NLP community. Most existing works treat ALD as regular classification with neural models, while ignoring the inherent conflicts of social media text that they are quite unnormalized and irregular. In this work, we targe...
Lexical chain consists of cohesion words in a document, which implies the underlying structure of a text, and thus facilitates downstream NLP tasks. Nevertheless, existing work focuses on detecting the simple surface lexicons with shallow syntax associations, ignoring the semantic-aware lexical compounds as well as the latent semantic frames, (e.g....
Aggressive language detection (ALD), detecting the abusive and offensive language in texts, is one of the crucial applications in NLP community. Most existing works treat ALD as regular classification with neural models, while ignoring the inherent conflicts of social media text that they are quite unnormalized and irregular. In this work, we targe...
Syntax has been shown useful for various NLP tasks, while existing work mostly encodes singleton syntactic tree using one hierarchical neural network. In this paper, we investigate a simple and effective method, Knowledge Distillation, to integrate heterogeneous structure knowledge into a unified sequential LSTM encoder. Experimental results on fou...
Current end-to-end semantic role labeling is mostly accomplished via graph-based neural models. However, these all are first-order models, where each decision for detecting any predicate-argument pair is made in isolation with local features. In this paper, we present a high-order refining mechanism to perform interaction between all predicate-argu...
We consider retrofitting structure-aware Transformer-based language model for facilitating end tasks by proposing to exploit syntactic distance to encode both the phrasal constituency and dependency connection into the language model. A middle-layer structural learning strategy is leveraged for structure integration, accomplished with main semantic...
Prior studies show that cross-lingual semantic role labeling (SRL) can be achieved by model transfer under the help of universal features. In this paper, we fill the gap of cross-lingual SRL by proposing an end-to-end SRL model that incorporates a variety of universal features and transfer methods. We study both the bilingual transfer and multi-sou...
Prior studies show that cross-lingual semantic role labeling (SRL) can be achieved by model transfer under the help of universal features. In this paper, we fill the gap of cross-lingual SRL by proposing an end-to-end SRL model that incorporates a variety of universal features and transfer methods. We study both the bilingual transfer and multi-sou...
Coreference resolution is one of the fundamental tasks in natural language processing (NLP), and is of great significance to understand the semantics of texts. Meanwhile, resolving coreference is essential for many NLP downstream applications. Existing methods largely focus on pronouns, possessives and noun phrases resolution in the general domain,...
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...
Nowadays graph neural networks have achieved excellent performance on many graph-based tasks such as abstract meaning representation (AMR) text generation and graph reasoning. Graph-based models often calculate the information flow by nodes and their associated edges. But node-based or edge-based calculation can not reflect the strong relation betw...
Slot filling and intent detection are the basic and crucial fields of natural language processing (NLP) for understanding and analyzing human language, owing to their wide applications in real-world scenarios. Most existing methods of slot filling and intent detection tasks utilize linear chain conditional random field (CRF) for only optimizing slo...
Biomedical information extraction (BioIE) is an important task. The aim is to analyze biomedical texts and extract structured information such as named entities and semantic relations between them. In recent years, pre-trained language models have largely improved the performance of BioIE. However, they neglect to incorporate external structural kn...
Identifying multiple emotions in a piece of text is an important research topic in the NLP community. Existing methods usually model the task as a multi-label classification problem, while these work has two issues. First, these methods fail to leverage the topic information of the text, which has been shown to be effective for sentiment analysis t...
Emojis are frequently used to express moods, emotions, and feelings in social media. There has been much research on emojis and sentiments. However, existing methods mainly face two limitations. First, they treat emojis as binary indicator features and rely on handcrafted features for emoji-based sentiment analysis. Second, they consider the sentim...
In recent years, the joint model of entity recognition (ER) and relation extraction (RE) has attracted more and more attention in the healthcare and medical domains. However, there are some problems with the prior work. The joint model cannot extract all the relations for a specific entity, and the majority of joint models heavily rely on complex a...
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Scientific information extraction is a crucial step for understanding scientific publications. In this paper, we focus on scientific keyphrase extraction, which aims to identify keyphrases from scientific articles and classify them into predefined categories. We present a neural network based approach for this task, which employs the bidirectional...
Normalizing disease names is a crucial task for biomedical and healthcare domains. Previous work explored various approaches, including rules, machine learning and deep learning, which focused on only one approach or one model. In this study, we systematically investigated the performances of various neural models and the effects of different featu...
While existing studies have established the relationship between electronic word-of-mouth (eWOM) and studio performance , limited research has been conducted to demonstrate how the attention-based model applies to the motion picture industry. In this study, examining a review corpus of seven Hollywood studios, we proved that deep learning with the...
Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding. Supervised approaches have achieved impressing performances when large-scale corpora are available for resource-rich languages such as English. While for the low-resource languages with no annotated SRL dataset, it is still chal...
The lack of human annotations has been one of the main obstacles for neural named entity recognition in low-resource domains. To address this problem, there have been many efforts on automatically generating silver annotations according to domain-specific dictionaries. However, the information of domain dictionaries is usually limited, and the gene...
In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition. First, we introduce the task in detail, showing the relationship with coreference recognition and differences from the existing tasks. Second, we propose an end-to-end joint model for the task, which exploits the...
Identifying multiple emotions in a sentence is an important research topic. Existing methods usually model the problem as multi-label classification task. However, previous methods have two issues, limiting the performance of the task. First, these models do not consider prior emotion distribution in a sentence. Second, they fail to effectively cap...
Emotion detection (ED) and emotion-cause pair extraction (ECPE) have drawn extensive research interests due to their wide applications in real-world scenarios. However, existing work fails to capture the implicit connection between two tasks. This limits the performances of these tasks. To address this issue, we propose a novel joint framework to t...
Conversational emotion recognition (CER) has attracted increasing interests in the natural language processing (NLP) community. Different from the vanilla emotion recognition, effective speaker-sensitive utterance representation is one major challenge for CER. In this paper, we exploit speaker identification (SI) as an auxiliary task to enhance the...
Keyphrases provide core information for users to understand the document. Most previous works utilize machine learning based methods for keyphrases extraction and achieve promising performance. However, these methods focus on identify keyphrases from the input text, and can not extract keyphrases that do not appear in the text. In this paper, we pr...