Chin-Yew Lin

Also published as: ChinYew Lin


2021

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Issues with Entailment-based Zero-shot Text Classification
Tingting Ma | Jin-Ge Yao | Chin-Yew Lin | Tiejun Zhao
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target label into a sentence of hypothesis and verifying whether or not it could be entailed by the input, aiming at generic classification applicable on any specified label space. In this opinion piece, we point out a few overlooked issues that are yet to be discussed in this line of work. We observe huge variance across different classification datasets amongst standard BERT-based NLI models and surprisingly find that pre-trained BERT without any fine-tuning can yield competitive performance against BERT fine-tuned for NLI. With the concern that these models heavily rely on spurious lexical patterns for prediction, we also experiment with preliminary approaches for more robust NLI, but the results are in general negative. Our observations reveal implicit but challenging difficulties in entailment-based zero-shot text classification.

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ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering
Shuang Chen | Qian Liu | Zhiwei Yu | Chin-Yew Lin | Jian-Guang Lou | Feng Jiang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing framework for large scale knowledge base question answering (KBQA). ReTraCk is designed as a modular framework to maintain high flexibility. It includes a retriever to retrieve relevant KB items efficiently, a transducer to generate logical form with syntax correctness guarantees and a checker to improve transduction procedure. ReTraCk is ranked at top1 overall performance on the GrailQA leaderboard and obtains highly competitive performance on the typical WebQuestionsSP benchmark. Our system can interact with users timely, demonstrating the efficiency of the proposed framework.

2020

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Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments
Feng Nie | Jinpeng Wang | Chin-Yew Lin
Proceedings of the 28th International Conference on Computational Linguistics

Learning semantic correspondences between structured input data (e.g., slot-value pairs) and associated texts is a core problem for many downstream NLP applications, e.g., data-to-text generation. Large-scale datasets recently proposed for generation contain loosely corresponding data text pairs, where part of spans in text cannot be aligned to its incomplete paired input. To learn semantic correspondences from such datasets, we propose a two-stage local-to-global alignment (L2GA) framework. First, a local model based on multi-instance learning is applied to build alignments for texts spans that can be directly grounded to its paired structured input. Then, a novel global model built upon a memory-guided conditional random field (CRF) layer aims to infer missing alignments for text spans which not supported by paired incomplete inputs, where the memory is designed to leverage alignment clues provided by the local model to strengthen the global model. In this way, the local model and global model can work jointly to learn semantic correspondences in the same framework. Experimental results show that our proposed method can be generalized to both restaurant and computer domains and improve the alignment accuracy.

2019

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Enhancing Neural Data-To-Text Generation Models with External Background Knowledge
Shuang Chen | Jinpeng Wang | Xiaocheng Feng | Feng Jiang | Bing Qin | Chin-Yew Lin
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge. They often assume that writing knowledge can be acquired from the training data alone. However, when people are writing, they not only rely on the data but also consider related knowledge. In this paper, we enhance neural data-to-text models with external knowledge in a simple but effective way to improve the fidelity of generated text. Besides relying on parallel data and text as in previous work, our model attends to relevant external knowledge, encoded as a temporary memory, and combines this knowledge with the context representation of data before generating words. This allows the model to infer relevant facts which are not explicitly stated in the data table from an external knowledge source. Experimental results on twenty-one Wikipedia infobox-to-text datasets show our model, KBAtt, consistently improves a state-of-the-art model on most of the datasets. In addition, to quantify when and why external knowledge is effective, we design a metric, KBGain, which shows a strong correlation with the observed performance boost. This result demonstrates the relevance of external knowledge and sparseness of original data are the main factors affecting system performance.

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An Encoder with non-Sequential Dependency for Neural Data-to-Text Generation
Feng Nie | Jinpeng Wang | Rong Pan | Chin-Yew Lin
Proceedings of the 12th International Conference on Natural Language Generation

Data-to-text generation aims to generate descriptions given a structured input data (i.e., a table with multiple records). Existing neural methods for encoding input data can be divided into two categories: a) pooling based encoders which ignore dependencies between input records or b) recurrent encoders which model only sequential dependencies between input records. In our investigation, although the recurrent encoder generally outperforms the pooling based encoder by learning the sequential dependencies, it is sensitive to the order of the input records (i.e., performance decreases when injecting the random shuffling noise over input data). To overcome this problem, we propose to adopt the self-attention mechanism to learn dependencies between arbitrary input records. Experimental results show the proposed method achieves comparable results and remains stable under random shuffling over input data.

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A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation
Feng Nie | Jin-Ge Yao | Jinpeng Wang | Rong Pan | Chin-Yew Lin
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent neural language generation systems often hallucinate contents (i.e., producing irrelevant or contradicted facts), especially when trained on loosely corresponding pairs of the input structure and text. To mitigate this issue, we propose to integrate a language understanding module for data refinement with self-training iterations to effectively induce strong equivalence between the input data and the paired text. Experiments on the E2E challenge dataset show that our proposed framework can reduce more than 50% relative unaligned noise from the original data-text pairs. A vanilla sequence-to-sequence neural NLG model trained on the refined data has improved on content correctness compared with the current state-of-the-art ensemble generator.

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Towards Improving Neural Named Entity Recognition with Gazetteers
Tianyu Liu | Jin-Ge Yao | Chin-Yew Lin
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Most of the recently proposed neural models for named entity recognition have been purely data-driven, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. This could increase the chance of overfitting since the models cannot access any supervision signal beyond the small amount of annotated data, limiting their power to generalize beyond the annotated entities. In this work, we show that properly utilizing external gazetteers could benefit segmental neural NER models. We add a simple module on the recently proposed hybrid semi-Markov CRF architecture and observe some promising results.

2018

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Neural Math Word Problem Solver with Reinforcement Learning
Danqing Huang | Jing Liu | Chin-Yew Lin | Jian Yin
Proceedings of the 27th International Conference on Computational Linguistics

Sequence-to-sequence model has been applied to solve math word problems. The model takes math problem descriptions as input and generates equations as output. The advantage of sequence-to-sequence model requires no feature engineering and can generate equations that do not exist in training data. However, our experimental analysis reveals that this model suffers from two shortcomings: (1) generate spurious numbers; (2) generate numbers at wrong positions. In this paper, we propose incorporating copy and alignment mechanism to the sequence-to-sequence model (namely CASS) to address these shortcomings. To train our model, we apply reinforcement learning to directly optimize the solution accuracy. It overcomes the “train-test discrepancy” issue of maximum likelihood estimation, which uses the surrogate objective of maximizing equation likelihood during training while the evaluation metric is solution accuracy (non-differentiable) at test time. Furthermore, to explore the effectiveness of our neural model, we use our model output as a feature and incorporate it into the feature-based model. Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.

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Aggregated Semantic Matching for Short Text Entity Linking
Feng Nie | Shuyan Zhou | Jing Liu | Jinpeng Wang | Chin-Yew Lin | Rong Pan
Proceedings of the 22nd Conference on Computational Natural Language Learning

The task of entity linking aims to identify concepts mentioned in a text fragments and link them to a reference knowledge base. Entity linking in long text has been well studied in previous work. However, short text entity linking is more challenging since the text are noisy and less coherent. To better utilize the local information provided in short texts, we propose a novel neural network framework, Aggregated Semantic Matching (ASM), in which two different aspects of semantic information between the local context and the candidate entity are captured via representation-based and interaction-based neural semantic matching models, and then two matching signals work jointly for disambiguation with a rank aggregation mechanism. Our evaluation shows that the proposed model outperforms the state-of-the-arts on public tweet datasets.

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Revisiting Distant Supervision for Relation Extraction
Tingsong Jiang | Jing Liu | Chin-Yew Lin | Zhifang Sui
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Using Intermediate Representations to Solve Math Word Problems
Danqing Huang | Jin-Ge Yao | Chin-Yew Lin | Qingyu Zhou | Jian Yin
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

To solve math word problems, previous statistical approaches attempt at learning a direct mapping from a problem description to its corresponding equation system. However, such mappings do not include the information of a few higher-order operations that cannot be explicitly represented in equations but are required to solve the problem. The gap between natural language and equations makes it difficult for a learned model to generalize from limited data. In this work we present an intermediate meaning representation scheme that tries to reduce this gap. We use a sequence-to-sequence model with a novel attention regularization term to generate the intermediate forms, then execute them to obtain the final answers. Since the intermediate forms are latent, we propose an iterative labeling framework for learning by leveraging supervision signals from both equations and answers. Our experiments show using intermediate forms outperforms directly predicting equations.

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Learning Latent Semantic Annotations for Grounding Natural Language to Structured Data
Guanghui Qin | Jin-Ge Yao | Xuening Wang | Jinpeng Wang | Chin-Yew Lin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Previous work on grounded language learning did not fully capture the semantics underlying the correspondences between structured world state representations and texts, especially those between numerical values and lexical terms. In this paper, we attempt at learning explicit latent semantic annotations from paired structured tables and texts, establishing correspondences between various types of values and texts. We model the joint probability of data fields, texts, phrasal spans, and latent annotations with an adapted semi-hidden Markov model, and impose a soft statistical constraint to further improve the performance. As a by-product, we leverage the induced annotations to extract templates for language generation. Experimental results suggest the feasibility of the setting in this study, as well as the effectiveness of our proposed framework.

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Operation-guided Neural Networks for High Fidelity Data-To-Text Generation
Feng Nie | Jinpeng Wang | Jin-Ge Yao | Rong Pan | Chin-Yew Lin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Recent neural models for data-to-text generation are mostly based on data-driven end-to-end training over encoder-decoder networks. Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data. This is a critical issue especially in domains that require inference or calculations over raw data. In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations. We propose a framework called Operation-guided Attention-based sequence-to-sequence network (OpAtt), with a specifically designed gating mechanism as well as a quantization module for operation results to utilize information from pre-executed operations. Experiments on two sports datasets show our proposed method clearly improves the fidelity of the generated texts to the input structured data.

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Data2Text Studio: Automated Text Generation from Structured Data
Longxu Dou | Guanghui Qin | Jinpeng Wang | Jin-Ge Yao | Chin-Yew Lin
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Data2Text Studio is a platform for automated text generation from structured data. It is equipped with a Semi-HMMs model to extract high-quality templates and corresponding trigger conditions from parallel data automatically, which improves the interactivity and interpretability of the generated text. In addition, several easy-to-use tools are provided for developers to edit templates of pre-trained models, and APIs are released for developers to call the pre-trained model to generate texts in third-party applications. We conduct experiments on RotoWire datasets for template extraction and text generation. The results show that our model achieves improvements on both tasks.

2017

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List-only Entity Linking
Ying Lin | Chin-Yew Lin | Heng Ji
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Traditional Entity Linking (EL) technologies rely on rich structures and properties in the target knowledge base (KB). However, in many applications, the KB may be as simple and sparse as lists of names of the same type (e.g., lists of products). We call it as List-only Entity Linking problem. Fortunately, some mentions may have more cues for linking, which can be used as seed mentions to bridge other mentions and the uninformative entities. In this work, we select most linkable mentions as seed mentions and disambiguate other mentions by comparing them with the seed mentions rather than directly with the entities. Our experiments on linking mentions to seven automatically mined lists show promising results and demonstrate the effectiveness of our approach.

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Learning Fine-Grained Expressions to Solve Math Word Problems
Danqing Huang | Shuming Shi | Chin-Yew Lin | Jian Yin
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper presents a novel template-based method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from training data. For each equation template, we automatically construct a rich template sketch by aggregating information from various problems with the same template. Our approach is implemented in a two-stage system. It first retrieves a few relevant equation system templates and aligns numbers in math word problems to those templates for candidate equation generation. It then does a fine-grained inference to obtain the final answer. Experiment results show that our method achieves an accuracy of 28.4% on the linear Dolphin18K benchmark, which is 10% (54% relative) higher than previous state-of-the-art systems while achieving an accuracy increase of 12% (59% relative) on the TS6 benchmark subset.

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A Statistical Framework for Product Description Generation
Jinpeng Wang | Yutai Hou | Jing Liu | Yunbo Cao | Chin-Yew Lin
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We present in this paper a statistical framework that generates accurate and fluent product description from product attributes. Specifically, after extracting templates and learning writing knowledge from attribute-description parallel data, we use the learned knowledge to decide what to say and how to say for product description generation. To evaluate accuracy and fluency for the generated descriptions, in addition to BLEU and Recall, we propose to measure what to say (in terms of attribute coverage) and to measure how to say (by attribute-specified generation) separately. Experimental results show that our framework is effective.

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Trust, but Verify! Better Entity Linking through Automatic Verification
Benjamin Heinzerling | Michael Strube | Chin-Yew Lin
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We introduce automatic verification as a post-processing step for entity linking (EL). The proposed method trusts EL system results collectively, by assuming entity mentions are mostly linked correctly, in order to create a semantic profile of the given text using geospatial and temporal information, as well as fine-grained entity types. This profile is then used to automatically verify each linked mention individually, i.e., to predict whether it has been linked correctly or not. Verification allows leveraging a rich set of global and pairwise features that would be prohibitively expensive for EL systems employing global inference. Evaluation shows consistent improvements across datasets and systems. In particular, when applied to state-of-the-art systems, our method yields an absolute improvement in linking performance of up to 1.7 F1 on AIDA/CoNLL’03 and up to 2.4 F1 on the English TAC KBP 2015 TEDL dataset.

2016

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News Citation Recommendation with Implicit and Explicit Semantics
Hao Peng | Jing Liu | Chin-Yew Lin
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation
Danqing Huang | Shuming Shi | Chin-Yew Lin | Jian Yin | Wei-Ying Ma
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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RBPB: Regularization-Based Pattern Balancing Method for Event Extraction
Lei Sha | Jing Liu | Chin-Yew Lin | Sujian Li | Baobao Chang | Zhifang Sui
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Knowledge Base Completion via Coupled Path Ranking
Quan Wang | Jing Liu | Yuanfei Luo | Bin Wang | Chin-Yew Lin
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

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LDTM: A Latent Document Type Model for Cumulative Citation Recommendation
Jingang Wang | Dandan Song | Zhiwei Zhang | Lejian Liao | Luo Si | Chin-Yew Lin
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Joint Entity Recognition and Disambiguation
Gang Luo | Xiaojiang Huang | Chin-Yew Lin | Zaiqing Nie
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Automatically Solving Number Word Problems by Semantic Parsing and Reasoning
Shuming Shi | Yuehui Wang | Chin-Yew Lin | Xiaojiang Liu | Yong Rui
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Context-aware Entity Morph Decoding
Boliang Zhang | Hongzhao Huang | Xiaoman Pan | Sujian Li | Chin-Yew Lin | Heng Ji | Kevin Knight | Zhen Wen | Yizhou Sun | Jiawei Han | Bulent Yener
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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Proceedings of the ACL-IJCNLP 2015 Student Research Workshop
Kuan-Yu Chen | Angelina Ivanova | Ellie Pavlick | Emily Bender | Chin-Yew Lin | Stephan Oepen
Proceedings of the ACL-IJCNLP 2015 Student Research Workshop

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Why Read if You Can Scan? Trigger Scoping Strategy for Biographical Fact Extraction
Dian Yu | Heng Ji | Sujian Li | Chin-Yew Lin
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Self-disclosure topic model for Twitter conversations
JinYeong Bak | Chin-Yew Lin | Alice Oh
Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media

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Unsupervised Template Mining for Semantic Category Understanding
Lei Shi | Shuming Shi | Chin-Yew Lin | Yi-Dong Shen | Yong Rui
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Self-disclosure topic model for classifying and analyzing Twitter conversations
JinYeong Bak | Chin-Yew Lin | Alice Oh
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Collective Tweet Wikification based on Semi-supervised Graph Regularization
Hongzhao Huang | Yunbo Cao | Xiaojiang Huang | Heng Ji | Chin-Yew Lin
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Question Difficulty Estimation in Community Question Answering Services
Jing Liu | Quan Wang | Chin-Yew Lin | Hsiao-Wuen Hon
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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A Hierarchical Entity-Based Approach to Structuralize User Generated Content in Social Media: A Case of Yahoo! Answers
Baichuan Li | Jing Liu | Chin-Yew Lin | Irwin King | Michael R. Lyu
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Learning a Replacement Model for Query Segmentation with Consistency in Search Logs
Wei Zhang | Yunbo Cao | Chin-Yew Lin | Jian Su | Chew-Lim Tan
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2012

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Ensemble Semantics for Large-scale Unsupervised Relation Extraction
Bonan Min | Shuming Shi | Ralph Grishman | Chin-Yew Lin
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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A Lazy Learning Model for Entity Linking using Query-Specific Information
Wei Zhang | Jian Su | Chew-Lim Tan | Yunbo Cao | Chin-Yew Lin
Proceedings of COLING 2012

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Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Haizhou Li | Chin-Yew Lin | Miles Osborne | Gary Geunbae Lee | Jong C. Park
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Haizhou Li | Chin-Yew Lin | Miles Osborne | Gary Geunbae Lee | Jong C. Park
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2011

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Nonlinear Evidence Fusion and Propagation for Hyponymy Relation Mining
Fan Zhang | Shuming Shi | Jing Liu | Shuqi Sun | Chin-Yew Lin
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Comparable Entity Mining from Comparative Questions
Shasha Li | Chin-Yew Lin | Young-In Song | Zhoujun Li
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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Efficient Inference of CRFs for Large-Scale Natural Language Data
Minwoo Jeong | Chin-Yew Lin | Gary Geunbae Lee
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

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A Structural Support Vector Method for Extracting Contexts and Answers of Questions from Online Forums
Wen-Yun Yang | Yunbo Cao | Chin-Yew Lin
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Semi-supervised Speech Act Recognition in Emails and Forums
Minwoo Jeong | Chin-Yew Lin | Gary Geunbae Lee
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2008

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Understanding and Summarizing Answers in Community-Based Question Answering Services
Yuanjie Liu | Shasha Li | Yunbo Cao | Chin-Yew Lin | Dingyi Han | Yong Yu
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Better Binarization for the CKY Parsing
Xinying Song | Shilin Ding | Chin-Yew Lin
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

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Searching Questions by Identifying Question Topic and Question Focus
Huizhong Duan | Yunbo Cao | Chin-Yew Lin | Yong Yu
Proceedings of ACL-08: HLT

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Using Conditional Random Fields to Extract Contexts and Answers of Questions from Online Forums
Shilin Ding | Gao Cong | Chin-Yew Lin | Xiaoyan Zhu
Proceedings of ACL-08: HLT

2007

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Sentence Level Machine Translation Evaluation as a Ranking
Yang Ye | Ming Zhou | Chin-Yew Lin
Proceedings of the Second Workshop on Statistical Machine Translation

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Low-Quality Product Review Detection in Opinion Summarization
Jingjing Liu | Yunbo Cao | Chin-Yew Lin | Yalou Huang | Ming Zhou
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

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Detecting Erroneous Sentences using Automatically Mined Sequential Patterns
Guihua Sun | Xiaohua Liu | Gao Cong | Ming Zhou | Zhongyang Xiong | John Lee | Chin-Yew Lin
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

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Topic Analysis for Psychiatric Document Retrieval
Liang-Chih Yu | Chung-Hsien Wu | Chin-Yew Lin | Eduard Hovy | Chia-Ling Lin
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Re-evaluating Machine Translation Results with Paraphrase Support
Liang Zhou | Chin-Yew Lin | Eduard Hovy
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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Automated Summarization Evaluation with Basic Elements.
Eduard Hovy | Chin-Yew Lin | Liang Zhou | Junichi Fukumoto
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

As part of evaluating a summary automati-cally, it is usual to determine how much of the contents of one or more human-produced “ideal” summaries it contains. Past automated methods such as ROUGE compare using fixed word ngrams, which are not ideal for a variety of reasons. In this paper we describe a framework in which summary evaluation measures can be instantiated and compared, and we implement a specific evaluation method using very small units of content, called Basic Elements that address some of the shortcomings of ngrams. This method is tested on DUC 2003, 2004, and 2005 systems and produces very good correlations with human judgments.

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Summarizing Answers for Complicated Questions
Liang Zhou | Chin-Yew Lin | Eduard Hovy
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Recent work in several computational linguistics (CL) applications (especially question answering) has shown the value of semantics (in fact, many people argue that the current performance ceiling experienced by so many CL applications derives from their inability to perform any kind of semantic processing). But the absence of a large semantic information repository that provides representations for sentences prevents the training of statistical CL engines and thus hampers the development of such semantics-enabled applications. This talk refers to recent work in several projects that seek to annotate large volumes of text with shallower or deeper representations of some semantic phenomena. It describes one of the essential problems—creating, managing, and annotating (at large scale) the meanings of words, and outlines the Omega ontology, being built at ISI, that acts as term repository. The talk illustrates how one can proceed from words via senses to concepts, and how the annotation process can help verify good concept decisions and expose bad ones. Much of this work is performed in the context of the OntoNotes project, joint with BBN, the Universities of Colorado and Pennsylvania, and ISI, that is working to build a corpus of about 1M words (English, Chinese, and Arabic), annotated for shallow semantics, over the next few years.

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ParaEval: Using Paraphrases to Evaluate Summaries Automatically
Liang Zhou | Chin-Yew Lin | Dragos Stefan Munteanu | Eduard Hovy
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

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An Information-Theoretic Approach to Automatic Evaluation of Summaries
Chin-Yew Lin | Guihong Cao | Jianfeng Gao | Jian-Yun Nie
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

2005

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Classummary: Introducing Discussion Summarization to Online Classrooms
Liang Zhou | Erin Shaw | Chin-Yew Lin | Eduard Hovy
Proceedings of HLT/EMNLP 2005 Interactive Demonstrations

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Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization
Jade Goldstein | Alon Lavie | Chin-Yew Lin | Clare Voss
Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization

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Automated Text Summarization
Chin-Yew Lin
Companion Volume to the Proceedings of Conference including Posters/Demos and tutorial abstracts

2004

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ROUGE: A Package for Automatic Evaluation of Summaries
Chin-Yew Lin
Text Summarization Branches Out

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Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics
Chin-Yew Lin | Franz Josef Och
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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ORANGE: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation
Chin-Yew Lin | Franz Josef Och
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics
Chin-Yew Lin | Eduard Hovy
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics

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iNeATS: Interactive Multi-Document Summarization
Anton Leuski | Chin-Yew Lin | Eduard Hovy
The Companion Volume to the Proceedings of 41st Annual Meeting of the Association for Computational Linguistics

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The Potential and Limitations of Automatic Sentence Extraction for Summarization
Chin-Yew Lin | Eduard Hovy
Proceedings of the HLT-NAACL 03 Text Summarization Workshop

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Improving Summarization Performance by Sentence Compression — A Pilot Study
Chin-Yew Lin
Proceedings of the Sixth International Workshop on Information Retrieval with Asian Languages

2002

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Manual and automatic evaluation of summaries
Chin-Yew Lin | Eduard Hovy
Proceedings of the ACL-02 Workshop on Automatic Summarization

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From Single to Multi-document Summarization
Chin-Yew Lin | Eduard Hovy
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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The Effectiveness of Dictionary and Web-Based Answer Reranking
Chin-Yew Lin
COLING 2002: The 19th International Conference on Computational Linguistics

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Using Knowledge to Facilitate Factoid Answer Pinpointing
Eduard Hovy | Ulf Hermjakob | Chin-Yew Lin | Deepak Ravichandran
COLING 2002: The 19th International Conference on Computational Linguistics

2001

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Toward Semantics-Based Answer Pinpointing
Eduard Hovy | Laurie Gerber | Ulf Hermjakob | Chin-Yew Lin | Deepak Ravichandran
Proceedings of the First International Conference on Human Language Technology Research

2000

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The Automated Acquisition of Topic Signatures for Text Summarization
Chin-Yew Lin | Eduard Hovy
COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics

1999

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Machine translation for information access across the language barrier: the MuST system
Chin-Yew Lin
Proceedings of Machine Translation Summit VII

In this paper we describe the design and implementation of MuST, a multilingual information retrieval, summarization, and translation system. MuST integrates machine translation and other text processing services to enable users to perform cross-language information retrieval using available search services such as commercial Internet search engines. To handle non-standard languages, a new Internet indexing agent can be deployed, specialized local search services can be built, and shallow MT can be added to provide useful functionality. A case study of augmenting MuST with Indonesian is included. MuST adopts ubiquitous web browsers as its primary user interface, and provides tightly integrated automated shallow translation and user biased summarization to help users quickly judge the relevance of documents.

1998

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Automated Text Summarization and the Summarist System
Eduard Hovy | Chin-Yew Lin
TIPSTER TEXT PROGRAM PHASE III: Proceedings of a Workshop held at Baltimore, Maryland, October 13-15, 1998

1997

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Identifying Topics by Position
Chin-Yew Lin | Eduard Hovy
Fifth Conference on Applied Natural Language Processing

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Automated Text Summarization in SUMMARIST
Eduard Hovy | ChinYew Lin
Intelligent Scalable Text Summarization

1995

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Knowledge-based Automatic Topic Identification
Chin-Yew Lin
33rd Annual Meeting of the Association for Computational Linguistics

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