Donghong Ji

Also published as: Dong Hong Ji, Dong-Hong Ji, DongHong Ji


2024

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Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information
Qiang Gao | Bobo Li | Zixiang Meng | Yunlong Li | Jun Zhou | Fei Li | Chong Teng | Donghong Ji
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Existing cross-document event coreference resolution models, which either compute mention similarity directly or enhance mention representation by extracting event arguments (such as location, time, agent, and patient), lackingmthe ability to utilize document-level information. As a result, they struggle to capture long-distance dependencies. This shortcoming leads to their underwhelming performance in determining coreference for the events where their argument information relies on long-distance dependencies. In light of these limitations, we propose the construction of document-level Rhetorical Structure Theory (RST) trees and cross-document Lexical Chains to model the structural and semantic information of documents. Subsequently, cross-document heterogeneous graphs are constructed and GAT is utilized to learn the representations of events. Finally, a pair scorer calculates the similarity between each pair of events and co-referred events can be recognized using standard clustering algorithm. Additionally, as the existing cross-document event coreference datasets are limited to English, we have developed a large-scale Chinese cross-document event coreference dataset to fill this gap, which comprises 53,066 event mentions and 4,476 clusters. After applying our model on the English and Chinese datasets respectively, it outperforms all baselines by large margins.

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What Factors Influence LLMs’ Judgments? A Case Study on Question Answering
Lei Chen | Bobo Li | Li Zheng | Haining Wang | Zixiang Meng | Runfeng Shi | Hao Fei | Jun Zhou | Fei Li | Chong Teng | Donghong Ji
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Large Language Models (LLMs) are now being considered as judges of high efficiency to evaluate the quality of answers generated by candidate models. However, their judgments may be influenced by complex scenarios and inherent biases, raising concerns about their reliability. This study aims to bridge this gap by introducing four unexplored factors and examining the performance of LLMs as judges, namely answer quantity, inducing statements, judging strategy, and judging style. Additionally, we introduce a new dimension of question difficulty to provide a more comprehensive understanding of LLMs’ judgments across varying question intricacies. We employ ChatGPT, GPT-4, Gemini, and Claude-2 as judges and conduct experiments on Vicuna Benchmark and MT-bench. Our study reveals that LLMs’ judging abilities are susceptible to the influence of these four factors, and analyzing from the newly proposed dimension of question difficulty is highly necessary. We also provide valuable insights into optimizing LLMs’ performance as judges, enhancing their reliability and adaptability across diverse evaluation scenarios.

2023

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FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing
Zhuang Li | Yuyang Chai | Terry Yue Zhuo | Lizhen Qu | Gholamreza Haffari | Fei Li | Donghong Ji | Quan Hung Tran
Findings of the Association for Computational Linguistics: ACL 2023

Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval. However, existing scene graph parsers that convert image captions into scene graphs often suffer from two types of errors. First, the generated scene graphs fail to capture the true semantics of the captions or the corresponding images, resulting in a lack of faithfulness. Second, the generated scene graphs have high inconsistency, with the same semantics represented by different annotations. To address these challenges, we propose a novel dataset, which involves re-annotating the captions in Visual Genome (VG) using a new intermediate representation called FACTUAL-MR. FACTUAL-MR can be directly converted into faithful and consistent scene graph annotations. Our experimental results clearly demonstrate that the parser trained on our dataset outperforms existing approaches in terms of faithfulness and consistency. This improvement leads to a significant performance boost in both image caption evaluation and zero-shot image retrieval tasks. Furthermore, we introduce a novel metric for measuring scene graph similarity, which, when combined with the improved scene graph parser, achieves state-of-the-art (SOTA) results on multiple benchmark datasets for the aforementioned tasks.

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DiaASQ: A Benchmark of Conversational Aspect-based Sentiment Quadruple Analysis
Bobo Li | Hao Fei | Fei Li | Yuhan Wu | Jinsong Zhang | Shengqiong Wu | Jingye Li | Yijiang Liu | Lizi Liao | Tat-Seng Chua | Donghong Ji
Findings of the Association for Computational Linguistics: ACL 2023

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. To bridge the gap between fine-grained sentiment analysis and conversational opinion mining, in this work, we introduce a novel task of conversational aspect-based sentiment quadruple analysis, namely DiaASQ, aiming to detect the quadruple of target-aspect-opinion-sentiment in a dialogue. We manually construct a large-scale high-quality DiaASQ dataset in both Chinese and English languages. We deliberately develop a neural model to benchmark the task, which advances in effectively performing end-to-end quadruple prediction, and manages to incorporate rich dialogue-specific and discourse feature representations for better cross-utterance quadruple extraction. We hope the new benchmark will spur more advancements in the sentiment analysis community.

2022

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Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis
Wenxuan Shi | Fei Li | Jingye Li | Hao Fei | Donghong Ji
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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 exacerbates the imbalance problem. (3) Two nodes in a dependency graph cannot have multiple arcs, therefore some overlapped sentiment tuples cannot be recognized. In this work, we propose nichetargeting solutions for these issues. First, we introduce a novel labeling strategy, which contains two sets of token pair labels, namely essential label set and whole label set. The essential label set consists of the basic labels for this task, which are relatively balanced and applied in the prediction layer. The whole label set includes rich labels to help our model capture various token relations, which are applied in the hidden layer to softly influence our model. Moreover, we also propose an effective model to well collaborate with our labeling strategy, which is equipped with the graph attention networks to iteratively refine token representations, and the adaptive multi-label classifier to dynamically predict multiple relations between token pairs. We perform extensive experiments on 5 benchmark datasets in four languages. Experimental results show that our model outperforms previous SOTA models by a large margin.

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Entity-centered Cross-document Relation Extraction
Fengqi Wang | Fei Li | Hao Fei | Jingye Li | Shengqiong Wu | Fangfang Su | Wenxuan Shi | Donghong Ji | Bo Cai
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

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 snippets surrounding target entities in multiple given documents, which brings considerable noisy and non-relevant sentences. Moreover, they utilize all the text paths in a document bag in a coarse-grained way, without considering the connections between these text paths.In this paper, we aim to address both of these shortages and push the state-of-the-art for cross-document RE. First, we focus on input construction for our RE model and propose an entity-based document-context filter to retain useful information in the given documents by using the bridge entities in the text paths. Second, we propose a cross-document RE model based on cross-path entity relation attention, which allow the entity relations across text paths to interact with each other. We compare our cross-document RE method with the state-of-the-art methods in the dataset CodRED. Our method outperforms them by at least 10% in F1, thus demonstrating its effectiveness.

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OneEE: A One-Stage Framework for Fast Overlapping and Nested Event Extraction
Hu Cao | Jingye Li | Fangfang Su | Fei Li | Hao Fei | Shengqiong Wu | Bobo Li | Liang Zhao | Donghong Ji
Proceedings of the 29th International Conference on Computational Linguistics

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 arguments,which suffer from error propagation. Therefore, we design a simple yet effective tagging scheme and model to formulate EE as word-word relation recognition, called OneEE. The relations between trigger or argument words are simultaneously recognized in one stage with parallel grid tagging, thus yielding a very fast event extraction speed. The model is equipped with an adaptive event fusion module to generate event-aware representations and a distance-aware predictor to integrate relative distance information for word-word relation recognition, which are empirically demonstrated to be effective mechanisms. Experiments on 3 overlapped and nested EE benchmarks, namely FewFC, Genia11, and Genia13, show that OneEE achieves the state-of-the-art (SOTA) results. Moreover, the inference speed of OneEE is faster than those of baselines in the same condition, and can be further substantially improved since it supports parallel inference.

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Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction
Shunjie Chen | Xiaochuan Shi | Jingye Li | Shengqiong Wu | Hao Fei | Fei Li | Donghong Ji
Proceedings of the 29th International Conference on Computational Linguistics

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 achieve state-of-the-art (SoTA) ECPE results. However, existing MTL-based methods either fail to simultaneously model the specific features and the interactive feature in between, or suffer from the inconsistency of label prediction. In this work, we consider addressing the above challenges for improving ECPE by performing two alignment mechanisms with a novel Aˆ2Net model. We first propose a feature-task alignment to explicitly model the specific emotion-&cause-specific features and the shared interactive feature. Besides, an inter-task alignment is implemented, in which the label distance between the ECPE and the combinations of EE&CE are learned to be narrowed for better label consistency. Evaluations of benchmarks show that our methods outperform current best-performing systems on all ECA subtasks. Further analysis proves the importance of our proposed alignment mechanisms for the task.

2021

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A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
Fei Li | ZhiChao Lin | Meishan Zhang | Donghong Ji
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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. First, entity fragments are recognized by traversing over all possible text spans, thus, overlapped entities can be recognized. Second, we perform relation classification to judge whether a given pair of entity fragments to be overlapping or succession. In this way, we can recognize not only discontinuous entities, and meanwhile doubly check the overlapped entities. As a whole, our model can be regarded as a relation extraction paradigm essentially. Experimental results on multiple benchmark datasets (i.e., CLEF, GENIA and ACE05) show that our model is highly competitive for overlapped and discontinuous NER.

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Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling
Hao Fei | Shengqiong Wu | Yafeng Ren | Fei Li | Donghong Ji
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction
Jingye Li | Kang Xu | Fei Li | Hao Fei | Yafeng Ren | Donghong Ji
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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AMR Parsing with Latent Structural Information
Qiji Zhou | Yue Zhang | Donghong Ji | Hao Tang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Abstract Meaning Representations (AMRs) capture sentence-level semantics structural representations to broad-coverage natural sentences. We investigate parsing AMR with explicit dependency structures and interpretable latent structures. We generate the latent soft structure without additional annotations, and fuse both dependency and latent structure via an extended graph neural networks. The fused structural information helps our experiments results to achieve the best reported results on both AMR 2.0 (77.5% Smatch F1 on LDC2017T10) and AMR 1.0 ((71.8% Smatch F1 on LDC2014T12).

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Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification
Hao Tang | Donghong Ji | Chenliang Li | Qiji Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Aspect-based sentiment classification is a popular task aimed at identifying the corresponding emotion of a specific aspect. One sentence may contain various sentiments for different aspects. Many sophisticated methods such as attention mechanism and Convolutional Neural Networks (CNN) have been widely employed for handling this challenge. Recently, semantic dependency tree implemented by Graph Convolutional Networks (GCN) is introduced to describe the inner connection between aspects and the associated emotion words. But the improvement is limited due to the noise and instability of dependency trees. To this end, we propose a dependency graph enhanced dual-transformer network (named DGEDT) by jointly considering the flat representations learnt from Transformer and graph-based representations learnt from the corresponding dependency graph in an iterative interaction manner. Specifically, a dual-transformer structure is devised in DGEDT to support mutual reinforcement between the flat representation learning and graph-based representation learning. The idea is to allow the dependency graph to guide the representation learning of the transformer encoder and vice versa. The results on five datasets demonstrate that the proposed DGEDT outperforms all state-of-the-art alternatives with a large margin.

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Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus
Hao Fei | Meishan Zhang | Donghong Ji
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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 challenging to obtain competitive performances. Cross-lingual SRL is one promising way to address the problem, which has achieved great advances with the help of model transferring and annotation projection. In this paper, we propose a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages from the source gold-standard SRL annotations. Experimental results on Universal Proposition Bank show that the translation-based method is highly effective, and the automatic pseudo datasets can improve the target-language SRL performances significantly.

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High-order Refining for End-to-end Chinese Semantic Role Labeling
Hao Fei | Yafeng Ren | Donghong Ji
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

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-argument pairs. Based on the baseline graph model, our high-order refining module learns higher-order features between all candidate pairs via attention calculation, which are later used to update the original token representations. After several iterations of refinement, the underlying token representations can be enriched with globally interacted features. Our high-order model achieves state-of-the-art results on Chinese SRL data, including CoNLL09 and Universal Proposition Bank, meanwhile relieving the long-range dependency issues.

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Improving Text Understanding via Deep Syntax-Semantics Communication
Hao Fei | Yafeng Ren | Donghong Ji
Findings of the Association for Computational Linguistics: EMNLP 2020

Recent studies show that integrating syntactic tree models with sequential semantic models can bring improved task performance, while these methods mostly employ shallow integration of syntax and semantics. In this paper, we propose a deep neural communication model between syntax and semantics to improve the performance of text understanding. Local communication is performed between syntactic tree encoder and sequential semantic encoder for mutual learning of information exchange. Global communication can further ensure comprehensive information propagation. Results on multiple syntax-dependent tasks show that our model outperforms strong baselines by a large margin. In-depth analysis indicates that our method is highly effective in composing sentence semantics.

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Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP
Hao Fei | Yafeng Ren | Donghong Ji
Findings of the Association for Computational Linguistics: EMNLP 2020

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 four typical syntax-dependent tasks show that our method outperforms tree encoders by effectively integrating rich heterogeneous structure syntax, meanwhile reducing error propagation, and also outperforms ensemble methods, in terms of both the efficiency and accuracy.

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Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions
Jingye Li | Hao Fei | Donghong Ji
Proceedings of the 28th International Conference on Computational Linguistics

In this paper, we target improving the joint dialogue act recognition (DAR) and sentiment classification (SC) tasks by fully modeling the local contexts of utterances. First, we employ the dynamic convolution network (DCN) as the utterance encoder to capture the dialogue contexts. Further, we propose a novel context-aware dynamic convolution network (CDCN) to better leverage the local contexts when dynamically generating kernels. We extended our frameworks into bi-channel version (i.e., BDCN and BCDCN) under multi-task learning to achieve the joint DAR and SC. Two channels can learn their own feature representations for DAR and SC, respectively, but with latent interaction. Besides, we suggest enhancing the tasks by employing the DiaBERT language model. Our frameworks obtain state-of-the-art performances against all baselines on two benchmark datasets, demonstrating the importance of modeling the local contexts.

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End to End Chinese Lexical Fusion Recognition with Sememe Knowledge
Yijiang Liu | Meishan Zhang | Donghong Ji
Proceedings of the 28th International Conference on Computational Linguistics

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 model for the task, handling mentions as well as coreference relationship jointly. The model exploits the state-of-the-art contextualized BERT representations as an encoder, and is further enhanced with the sememe knowledge from HowNet by graph attention networks. We manually annotate a benchmark dataset for the task and then conduct experiments on it. Results demonstrate that our final model is effective and competitive for the task. Detailed analysis is offered for comprehensively understanding the new task and our proposed model.

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HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations
Jingye Li | Donghong Ji | Fei Li | Meishan Zhang | Yijiang Liu
Proceedings of the 28th International Conference on Computational Linguistics

Emotion detection in conversations (EDC) is to detect the emotion for each utterance in conversations that have multiple speakers. Different from the traditional non-conversational emotion detection, the model for EDC should be context-sensitive (e.g., understanding the whole conversation rather than one utterance) and speaker-sensitive (e.g., understanding which utterance belongs to which speaker). In this paper, we propose a transformer-based context- and speaker-sensitive model for EDC, namely HiTrans, which consists of two hierarchical transformers. We utilize BERT as the low-level transformer to generate local utterance representations, and feed them into another high-level transformer so that utterance representations could be sensitive to the global context of the conversation. Moreover, we exploit an auxiliary task to make our model speaker-sensitive, called pairwise utterance speaker verification (PUSV), which aims to classify whether two utterances belong to the same speaker. We evaluate our model on three benchmark datasets, namely EmoryNLP, MELD and IEMOCAP. Results show that our model outperforms previous state-of-the-art models.

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Retrofitting Structure-aware Transformer Language Model for End Tasks
Hao Fei | Yafeng Ren | Donghong Ji
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We consider retrofitting structure-aware Transformer 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 task training under multi-task learning scheme. Experimental results show that the retrofitted structure-aware Transformer language model achieves improved perplexity, meanwhile inducing accurate syntactic phrases. By performing structure-aware fine-tuning, our model achieves significant improvements for both semantic- and syntactic-dependent tasks.

2019

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Bacteria Biotope Relation Extraction via Lexical Chains and Dependency Graphs
Wuti Xiong | Fei Li | Ming Cheng | Hong Yu | Donghong Ji
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks

abstract In this article, we describe our approach for the Bacteria Biotopes relation extraction (BB-rel) subtask in the BioNLP Shared Task 2019. This task aims to promote the development of text mining systems that extract relationships between Microorganism, Habitat and Phenotype entities. In this paper, we propose a novel approach for dependency graph construction based on lexical chains, so one dependency graph can represent one or multiple sentences. After that, we propose a neural network model which consists of the bidirectional long short-term memories and an attention graph convolution neural network to learn relation extraction features from the graph. Our approach is able to extract both intra- and inter-sentence relations, and meanwhile utilize syntax information. The results show that our approach achieved the best F1 (66.3%) in the official evaluation participated by 7 teams.

2016

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WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models
Xin Tang | Fei Li | Donghong Ji
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Multi-prototype Chinese Character Embedding
Yanan Lu | Yue Zhang | Donghong Ji
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Chinese sentences are written as sequences of characters, which are elementary units of syntax and semantics. Characters are highly polysemous in forming words. We present a position-sensitive skip-gram model to learn multi-prototype Chinese character embeddings, and explore the usefulness of such character embeddings to Chinese NLP tasks. Evaluation on character similarity shows that multi-prototype embeddings are significantly better than a single-prototype baseline. In addition, used as features in the Chinese NER task, the embeddings result in a 1.74% F-score improvement over a state-of-the-art baseline.

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Distance Metric Learning for Aspect Phrase Grouping
Shufeng Xiong | Yue Zhang | Donghong Ji | Yinxia Lou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Aspect phrase grouping is an important task in aspect-level sentiment analysis. It is a challenging problem due to polysemy and context dependency. We propose an Attention-based Deep Distance Metric Learning (ADDML) method, by considering aspect phrase representation as well as context representation. First, leveraging the characteristics of the review text, we automatically generate aspect phrase sample pairs for distant supervision. Second, we feed word embeddings of aspect phrases and their contexts into an attention-based neural network to learn feature representation of contexts. Both aspect phrase embedding and context embedding are used to learn a deep feature subspace for measure the distances between aspect phrases for K-means clustering. Experiments on four review datasets show that the proposed method outperforms state-of-the-art strong baseline methods.

2015

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A Transition-based Model for Joint Segmentation, POS-tagging and Normalization
Tao Qian | Yue Zhang | Meishan Zhang | Yafeng Ren | Donghong Ji
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Event-Driven Headline Generation
Rui Sun | Yue Zhang | Meishan Zhang | Donghong Ji
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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Word Sense Induction Using Lexical Chain based Hypergraph Model
Tao Qian | Donghong Ji | Mingyao Zhang | Chong Teng | Congling Xia
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Positive Unlabeled Learning for Deceptive Reviews Detection
Yafeng Ren | Donghong Ji | Hongbin Zhang
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2012

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Context-Enhanced Personalized Social Summarization
Po Hu | Donghong Ji | Chong Teng | Yujing Guo
Proceedings of COLING 2012

2011

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Social Summarization via Automatically Discovered Social Context
Po Hu | Cheng Sun | Longfei Wu | Donghong Ji | Chong Teng
Proceedings of 5th International Joint Conference on Natural Language Processing

2009

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Query-Focused Multi-Document Summarization Using Co-Training Based Semi-Supervised Learning
Po Hu | Donghong Ji | Hai Wang | Chong Teng
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1

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Finding Answers to Definition Questions Using Web Knowledge Bases
Han Ren | Donghong Ji | Jing Wan | Chong Teng
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

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Document Re-ranking via Wikipedia Articles for Definition/Biography Type Questions
Maofu Liu | Fang Fang | Donghong Ji
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

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Parsing Syntactic and Semantic Dependencies for Multiple Languages with A Pipeline Approach
Han Ren | Donghong Ji | Jing Wan | Mingyao Zhang
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task

2008

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A Research on Automatic Chinese Catchword Extraction
Han Ren | Donghong Ji | Lei Han
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Catchwords refer to popular words or phrases within certain area in certain period of time. In this paper, we propose a novel approach for automatic Chinese catchwords extraction. At the beginning, we discuss the linguistic definition of catchwords and analyze the features of catchwords by manual evaluation. According to those features of catchwords, we define three aspects to describe Popular Degree of catchwords. To extract terms with maximum meaning, we adopt an effective ATE algorithm for multi-character words and long phrases. Then we use conic fitting in Time Series Analysis to build Popular Degree Curves of extracted terms. To calculate Popular Degree Values of catchwords, a formula is proposed which includes values of Popular Trend, Peak Value and Popular Keeping. Finally, a ranking list of catchword candidates is built according to Popular Degree Values. Experiments show that automatic Chinese catchword extraction is effective and objective in comparison with manual evaluation.

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Sentence Ordering based on Cluster Adjacency in Multi-Document Summarization
Donghong Ji | Yu Nie
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

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Automatic Chinese Catchword Extraction Based on Time Series Analysis
Han Ren | Donghong Ji | Jing Wan | Lei Han
CoNLL 2008: Proceedings of the Twelfth Conference on Computational Natural Language Learning

2007

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Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Information
GuoDong Zhou | Min Zhang | Dong Hong Ji | QiaoMing Zhu
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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I2R: Three Systems for Word Sense Discrimination, Chinese Word Sense Disambiguation, and English Word Sense Disambiguation
Zheng-Yu Niu | Dong-Hong Ji | Chew-Lim Tan
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

2006

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Relation Extraction Using Label Propagation Based Semi-Supervised Learning
Jinxiu Chen | Donghong Ji | Chew Lim Tan | Zhengyu Niu
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Unsupervised Relation Disambiguation Using Spectral Clustering
Jinxiu Chen | Donghong Ji | Chew Lim Tan | Zhengyu Niu
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Semi-supervised Relation Extraction with Label Propagation
Jinxiu Chen | Donghong Ji | Chew Lim Tan | Zhengyu Niu
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

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Chinese Word Segmentation and Named Entity Recognition Based on a Context-Dependent Mutual Information Independence Model
Min Zhang | GuoDong Zhou | LingPeng Yang | DongHong Ji
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing

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Partially Supervised Sense Disambiguation by Learning Sense Number from Tagged and Untagged Corpora
Zheng-Yu Niu | Dong-Hong Ji | Chew Lim Tan
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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Unsupervised Relation Disambiguation with Order Identification Capabilities
Jinxiu Chen | Donghong Ji | Chew Lim Tan | Zhengyu Niu
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

2005

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Automatic Relation Extraction with Model Order Selection and Discriminative Label Identification
Jinxiu Chen | Donghong Ji | Chew Lim Tan | Zhengyu Niu
Second International Joint Conference on Natural Language Processing: Full Papers

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Unsupervised Feature Selection for Relation Extraction
Jinxiu Chen | Donghong Ji | Chew Lim Tan | Zhengyu Niu
Companion Volume to the Proceedings of Conference including Posters/Demos and tutorial abstracts

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Word Sense Disambiguation Using Label Propagation Based Semi-Supervised Learning
Zheng-Yu Niu | Dong-Hong Ji | Chew Lim Tan
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

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An Unsupervised Approach to Chinese Word Sense Disambiguation Based on Hownet
Hao Chen | Tingting He | Donghong Ji | Changqin Quan
International Journal of Computational Linguistics & Chinese Language Processing, Volume 10, Number 4, December 2005: Special Issue on Selected Papers from CLSW-5

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A Semi-Supervised Feature Clustering Algorithm with Application to Word Sense Disambiguation
Zheng-Yu Niu | Dong-Hong Ji | Chew Lim Tan
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

2004

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Learning Word Sense With Feature Selection and Order Identification Capabilities
Zheng-Yu Niu | Dong-Hong Ji | Chew-Lim Tan
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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A Model of Semantic Representations Analysis for Chinese Sentences
Li Tang | Donghong Ji | Lingpeng Yang | Yu Nie
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Building a Conceptual Graph Bank for Chinese Language
Donghong Ji | Li Tang | Lingpeng Yang
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Document Re-ranking Based on Automatically Acquired Key Terms in Chinese Information Retrieval
Lingpeng Yang | Donghong Ji | Li Tang
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Optimizing feature set for Chinese Word Sense Disambiguation
Zheng-Yu Niu | Dong-Hong Ji | Chew-Lim Tan
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text

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Chinese Text Summarization Based on Thematic Area Detection
Po Hu | Tingting He | Donghong Ji
Text Summarization Branches Out

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Document Re-ranking based on Global and Local Terms
Lingpeng Yang | DongHong Ji | Li Tang
Proceedings of the Third SIGHAN Workshop on Chinese Language Processing

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A Large-Scale Semantic Structure for Chinese Sentences
Li Tang | Donghong Ji | Lingpeng Yang
Proceedings of the Third SIGHAN Workshop on Chinese Language Processing

2003

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Proceedings of the 17th Pacific Asia Conference on Language, Information and Computation
Dong Hong Ji | Kim Teng Lua
Proceedings of the 17th Pacific Asia Conference on Language, Information and Computation

2000

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Semantic Annotation of Chinese Phrases Using Recursive Graph
Donghong Ji
Second Chinese Language Processing Workshop

1998

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Combining a Chinese Thesaurus with a Chinese Dictionary
Donghong Ji | Junping Gong | Changning Huang
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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Combining a Chinese Thesaurus with a Chinese Dictionary
Donghong Ji | Junping Gong | Changning Huang
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

1997

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Word Sense Disambiguation Based on Structured Semantic Space
Donghong Ji | Changning Huang
Second Conference on Empirical Methods in Natural Language Processing

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Learning New Compositions from Given Ones
Donghong Ji | Jun He | Changning Huang
CoNLL97: Computational Natural Language Learning