Chen Li


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Graph Neural News Recommendation with Unsupervised Preference Disentanglement
Linmei Hu | Siyong Xu | Chen Li | Cheng Yang | Chuan Shi | Nan Duan | Xing Xie | Ming Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

With the explosion of news information, personalized news recommendation has become very important for users to quickly find their interested contents. Most existing methods usually learn the representations of users and news from news contents for recommendation. However, they seldom consider high-order connectivity underlying the user-news interactions. Moreover, existing methods failed to disentangle a user’s latent preference factors which cause her clicks on different news. In this paper, we model the user-news interactions as a bipartite graph and propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement, named GNUD. Our model can encode high-order relationships into user and news representations by information propagation along the graph. Furthermore, the learned representations are disentangled with latent preference factors by a neighborhood routing algorithm, which can enhance expressiveness and interpretability. A preference regularizer is also designed to force each disentangled subspace to independently reflect an isolated preference, improving the quality of the disentangled representations. Experimental results on real-world news datasets demonstrate that our proposed model can effectively improve the performance of news recommendation and outperform state-of-the-art news recommendation methods.

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Chunk-based Chinese Spelling Check with Global Optimization
Zuyi Bao | Chen Li | Rui Wang
Findings of the Association for Computational Linguistics: EMNLP 2020

Chinese spelling check is a challenging task due to the characteristics of the Chinese language, such as the large character set, no word boundary, and short word length. On the one hand, most of the previous works only consider corrections with similar character pronunciation or shape, failing to correct visually and phonologically irrelevant typos. On the other hand, pipeline-style architectures are widely adopted to deal with different types of spelling errors in individual modules, which is difficult to optimize. In order to handle these issues, in this work, 1) we extend the traditional confusion sets with semantical candidates to cover different types of errors; 2) we propose a chunk-based framework to correct single-character and multi-character word errors uniformly; and 3) we adopt a global optimization strategy to enable a sentence-level correction selection. The experimental results show that the proposed approach achieves a new state-of-the-art performance on three benchmark datasets, as well as an optical character recognition dataset.

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Understanding User Resistance Strategies in Persuasive Conversations
Youzhi Tian | Weiyan Shi | Chen Li | Zhou Yu
Findings of the Association for Computational Linguistics: EMNLP 2020

Persuasive dialog systems have various usages, such as donation persuasion and physical exercise persuasion. Previous persuasive dialog systems research mostly focused on analyzing the persuader’s strategies and paid little attention to the persuadee (user). However, understanding and addressing users’ resistance strategies is an essential job of a persuasive dialog system. So, we adopt a preliminary framework on persuasion resistance in psychology and design a fine-grained resistance strategy annotation scheme. We annotate the PersuasionForGood dataset with the scheme. With the enriched annotations, we build a classifier to predict the resistance strategies. Furthermore, we analyze the relationships between persuasion strategies and persuasion resistance strategies. Our work lays the ground for developing a persuasive dialogue system that can understand and address user resistance strategy appropriately. The code and data will be released.

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Better Highlighting: Creating Sub-Sentence Summary Highlights
Sangwoo Cho | Kaiqiang Song | Chen Li | Dong Yu | Hassan Foroosh | Fei Liu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Amongst the best means to summarize is highlighting. In this paper, we aim to generate summary highlights to be overlaid on the original documents to make it easier for readers to sift through a large amount of text. The method allows summaries to be understood in context to prevent a summarizer from distorting the original meaning, of which abstractive summarizers usually fall short. In particular, we present a new method to produce self-contained highlights that are understandable on their own to avoid confusion. Our method combines determinantal point processes and deep contextualized representations to identify an optimal set of sub-sentence segments that are both important and non-redundant to form summary highlights. To demonstrate the flexibility and modeling power of our method, we conduct extensive experiments on summarization datasets. Our analysis provides evidence that highlighting is a promising avenue of research towards future summarization.


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Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations
Zuyi Bao | Rui Huang | Chen Li | Kenny Zhu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Previous work on cross-lingual sequence labeling tasks either requires parallel data or bridges the two languages through word-by-word matching. Such requirements and assumptions are infeasible for most languages, especially for languages with large linguistic distances, e.g., English and Chinese. In this work, we propose a Multilingual Language Model with deep semantic Alignment (MLMA) to generate language-independent representations for cross-lingual sequence labeling. Our methods require only monolingual corpora with no bilingual resources at all and take advantage of deep contextualized representations. Experimental results show that our approach achieves new state-of-the-art NER and POS performance across European languages, and is also effective on distant language pairs such as English and Chinese.

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Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations
Sangwoo Cho | Chen Li | Dong Yu | Hassan Foroosh | Fei Liu
Proceedings of the 2nd Workshop on New Frontiers in Summarization

Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select a most probable set of summary sentences according to a probabilistic measure defined by respectively modeling sentence prominence and pairwise repulsion. Traditionally, both aspects are modelled using shallow and linguistically informed features, but the rise of deep contextualized representations raises an interesting question. Whether, and to what extent, could contextualized sentence representations be used to improve the DPP framework? Our findings suggest that, despite the success of deep semantic representations, it remains necessary to combine them with surface indicators for effective identification of summary-worthy sentences.

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BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
Eva Sharma | Chen Li | Lu Wang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure. In such datasets, summary-worthy content often appears in the beginning of input articles. Moreover, large segments from input articles are present verbatim in their respective summaries. These issues impede the learning and evaluation of systems that can understand an article’s global content structure as well as produce abstractive summaries with high compression ratio. In this work, we present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. Compared to existing summarization datasets, BIGPATENT has the following properties: i) summaries contain a richer discourse structure with more recurring entities, ii) salient content is evenly distributed in the input, and iii) lesser and shorter extractive fragments are present in the summaries. Finally, we train and evaluate baselines and popular learning models on BIGPATENT to shed light on new challenges and motivate future directions for summarization research.

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Extracting Kinship from Obituary to Enhance Electronic Health Records for Genetic Research
Kai He | Jialun Wu | Xiaoyong Ma | Chong Zhang | Ming Huang | Chen Li | Lixia Yao
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

Claims database and electronic health records database do not usually capture kinship or family relationship information, which is imperative for genetic research. We identify online obituaries as a new data source and propose a special named entity recognition and relation extraction solution to extract names and kinships from online obituaries. Built on 1,809 annotated obituaries and a novel tagging scheme, our joint neural model achieved macro-averaged precision, recall and F measure of 72.69%, 78.54% and 74.93%, and micro-averaged precision, recall and F measure of 95.74%, 98.25% and 96.98% using 57 kinships with 10 or more examples in a 10-fold cross-validation experiment. The model performance improved dramatically when trained with 34 kinships with 50 or more examples. Leveraging additional information such as age, death date, birth date and residence mentioned by obituaries, we foresee a promising future of supplementing EHR databases with comprehensive and accurate kinship information for genetic research.


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DM_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic features
Chunping Ma | Huafei Zheng | Pengjun Xie | Chen Li | Linlin Li | Luo Si
Proceedings of The 12th International Workshop on Semantic Evaluation

This paper describes our submissions for SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using NLP. The DM_NLP participated in two subtasks: SubTask 1 classifies if a sentence is useful for inferring malware actions and capabilities, and SubTask 2 predicts token labels (“Action”, “Entity”, “Modifier” and “Others”) for a given malware-related sentence. Since we leverage results of Subtask 2 directly to infer the result of Subtask 1, the paper focus on the system solving Subtask 2. By taking Subtask 2 as a sequence labeling task, our system relies on a recurrent neural network named BiLSTM-CNN-CRF with rich linguistic features, such as POS tags, dependency parsing labels, chunking labels, NER labels, Brown clustering. Our system achieved the highest F1 score in both token level and phrase level.

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A Hybrid System for Chinese Grammatical Error Diagnosis and Correction
Chen Li | Junpei Zhou | Zuyi Bao | Hengyou Liu | Guangwei Xu | Linlin Li
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

This paper introduces the DM_NLP team’s system for NLPTEA 2018 shared task of Chinese Grammatical Error Diagnosis (CGED), which can be used to detect and correct grammatical errors in texts written by Chinese as a Foreign Language (CFL) learners. This task aims at not only detecting four types of grammatical errors including redundant words (R), missing words (M), bad word selection (S) and disordered words (W), but also recommending corrections for errors of M and S types. We proposed a hybrid system including four models for this task with two stages: the detection stage and the correction stage. In the detection stage, we first used a BiLSTM-CRF model to tag potential errors by sequence labeling, along with some handcraft features. Then we designed three Grammatical Error Correction (GEC) models to generate corrections, which could help to tune the detection result. In the correction stage, candidates were generated by the three GEC models and then merged to output the final corrections for M and S types. Our system reached the highest precision in the correction subtask, which was the most challenging part of this shared task, and got top 3 on F1 scores for position detection of errors.


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XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter
Yazhou Hao | YangYang Lan | Yufei Li | Chen Li
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the XJSA System submission from XJTU. Our system was created for SemEval2017 Task 4 – subtask A which is very popular and fundamental. The system is based on convolutional neural network and word embedding. We used two pre-trained word vectors and adopt a dynamic strategy for k-max pooling.

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XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model
Yu Long | Zhijing Li | Xuan Wang | Chen Li
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

Temporality is crucial in understanding the course of clinical events from a patient’s electronic health recordsand temporal processing is becoming more and more important for improving access to content.SemEval 2017 Task 12 (Clinical TempEval) addressed this challenge using the THYME corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 guidelines. We developed and evaluated approaches for: extraction of temporal expressions (TIMEX3) and EVENTs; EVENT attributes; document-time relations. Our approach is a hybrid model which is based on rule based methods, semi-supervised learning, and semantic features with addition of manually crafted rules.


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Using Relevant Public Posts to Enhance News Article Summarization
Chen Li | Zhongyu Wei | Yang Liu | Yang Jin | Fei Huang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

A news article summary usually consists of 2-3 key sentences that reflect the gist of that news article. In this paper we explore using public posts following a new article to improve automatic summary generation for the news article. We propose different approaches to incorporate information from public posts, including using frequency information from the posts to re-estimate bigram weights in the ILP-based summarization model and to re-weight a dependency tree edge’s importance for sentence compression, directly selecting sentences from posts as the final summary, and finally a strategy to combine the summarization results generated from news articles and posts. Our experiments on data collected from Facebook show that relevant public posts provide useful information and can be effectively leveraged to improve news article summarization results.

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A Preliminary Study of Disputation Behavior in Online Debating Forum
Zhongyu Wei | Yandi Xia | Chen Li | Yang Liu | Zachary Stallbohm | Yi Li | Yang Jin
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

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LitWay, Discriminative Extraction for Different Bio-Events
Chen Li | Zhiqiang Rao | Xiangrong Zhang
Proceedings of the 4th BioNLP Shared Task Workshop


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Detecting Causally Embedded Structures Using an Evolutionary Algorithm
Chen Li | Roxana Girju
Proceedings of the The 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation

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Using word embedding for bio-event extraction
Chen Li | Runqing Song | Maria Liakata | Andreas Vlachos | Stephanie Seneff | Xiangrong Zhang
Proceedings of BioNLP 15

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Using External Resources and Joint Learning for Bigram Weighting in ILP-Based Multi-Document Summarization
Chen Li | Yang Liu | Lin Zhao
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Improving Update Summarization via Supervised ILP and Sentence Reranking
Chen Li | Yang Liu | Lin Zhao
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Improving Named Entity Recognition in Tweets via Detecting Non-Standard Words
Chen Li | Yang Liu
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)

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Using Tweets to Help Sentence Compression for News Highlights Generation
Zhongyu Wei | Yang Liu | Chen Li | Wei Gao
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Sharing annotations better: RESTful Open Annotation
Sampo Pyysalo | Jorge Campos | Juan Miguel Cejuela | Filip Ginter | Kai Hakala | Chen Li | Pontus Stenetorp | Lars Juhl Jensen
Proceedings of ACL-IJCNLP 2015 System Demonstrations


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Improving Text Normalization via Unsupervised Model and Discriminative Reranking
Chen Li | Yang Liu
Proceedings of the ACL 2014 Student Research Workshop

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Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees
Chen Li | Yang Liu | Fei Liu | Lin Zhao | Fuliang Weng
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


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Document Summarization via Guided Sentence Compression
Chen Li | Fei Liu | Fuliang Weng | Yang Liu
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Using Supervised Bigram-based ILP for Extractive Summarization
Chen Li | Xian Qian | Yang Liu
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Improving Text Normalization using Character-Blocks Based Models and System Combination
Chen Li | Yang Liu
Proceedings of COLING 2012


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Mining the Web for Reciprocal Relationships
Michael Paul | Roxana Girju | Chen Li
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)


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Statistical Analysis on Large Scale Chinese Short Message Corpus and Automatic Short Message Error Correction
Rile Hu | Yuezhong Tang | Chen Li | Xia Wang
Proceedings of the 22nd Pacific Asia Conference on Language, Information and Computation