Jing Jiang


2021

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Cross-Topic Rumor Detection using Topic-Mixtures
Xiaoying Ren | Jing Jiang | Ling Min Serena Khoo | Hai Leong Chieu
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

There has been much interest in rumor detection using deep learning models in recent years. A well-known limitation of deep learning models is that they tend to learn superficial patterns, which restricts their generalization ability. We find that this is also true for cross-topic rumor detection. In this paper, we propose a method inspired by the “mixture of experts” paradigm. We assume that the prediction of the rumor class label given an instance is dependent on the topic distribution of the instance. After deriving a vector representation for each topic, given an instance, we derive a “topic mixture” vector for the instance based on its topic distribution. This topic mixture is combined with the vector representation of the instance itself to make rumor predictions. Our experiments show that our proposed method can outperform two baseline debiasing methods in a cross-topic setting. In a synthetic setting when we removed topic-specific words, our method also works better than the baselines, showing that our method does not rely on superficial features.

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COSY: COunterfactual SYntax for Cross-Lingual Understanding
Sicheng Yu | Hao Zhang | Yulei Niu | Qianru Sun | Jing Jiang
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)

Pre-trained multilingual language models, e.g., multilingual-BERT, are widely used in cross-lingual tasks, yielding the state-of-the-art performance. However, such models suffer from a large performance gap between source and target languages, especially in the zero-shot setting, where the models are fine-tuned only on English but tested on other languages for the same task. We tackle this issue by incorporating language-agnostic information, specifically, universal syntax such as dependency relations and POS tags, into language models, based on the observation that universal syntax is transferable across different languages. Our approach, called COunterfactual SYntax (COSY), includes the design of SYntax-aware networks as well as a COunterfactual training method to implicitly force the networks to learn not only the semantics but also the syntax. To evaluate COSY, we conduct cross-lingual experiments on natural language inference and question answering using mBERT and XLM-R as network backbones. Our results show that COSY achieves the state-of-the-art performance for both tasks, without using auxiliary training data.

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Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering
Yunshi Lan | Jing Jiang
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)

Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking. Our experiments on two datasets demonstrate the effectiveness of our proposed method.

2020

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RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion
Hao Huang | Guodong Long | Tao Shen | Jing Jiang | Chengqi Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Many graph embedding approaches have been proposed for knowledge graph completion via link prediction. Among those, translating embedding approaches enjoy the advantages of light-weight structure, high efficiency and great interpretability. Especially when extended to complex vector space, they show the capability in handling various relation patterns including symmetry, antisymmetry, inversion and composition. However, previous translating embedding approaches defined in complex vector space suffer from two main issues: 1) representing and modeling capacities of the model are limited by the translation function with rigorous multiplication of two complex numbers; and 2) embedding ambiguity caused by one-to-many relations is not explicitly alleviated. In this paper, we propose a relation-adaptive translation function built upon a novel weighted product in complex space, where the weights are learnable, relation-specific and independent to embedding size. The translation function only requires eight more scalar parameters each relation, but improves expressive power and alleviates embedding ambiguity problem. Based on the function, we then present our Relation-adaptive translating Embedding (RatE) approach to score each graph triple. Moreover, a novel negative sampling method is proposed to utilize both prior knowledge and self-adversarial learning for effective optimization. Experiments verify RatE achieves state-of-the-art performance on four link prediction benchmarks.

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A BERT-based Dual Embedding Model for Chinese Idiom Prediction
Minghuan Tan | Jing Jiang
Proceedings of the 28th International Conference on Computational Linguistics

Chinese idioms are special fixed phrases usually derived from ancient stories, whose meanings are oftentimes highly idiomatic and non-compositional. The Chinese idiom prediction task is to select the correct idiom from a set of candidate idioms given a context with a blank. We propose a BERT-based dual embedding model to encode the contextual words as well as to learn dual embeddings of the idioms. Specifically, we first match the embedding of each candidate idiom with the hidden representation corresponding to the blank in the context. We then match the embedding of each candidate idiom with the hidden representations of all the tokens in the context thorough context pooling. We further propose to use two separate idiom embeddings for the two kinds of matching. Experiments on a recently released Chinese idiom cloze test dataset show that our proposed method performs better than the existing state of the art. Ablation experiments also show that both context pooling and dual embedding contribute to the improvement of performance.

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Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention
Yang Li | Tao Shen | Guodong Long | Jing Jiang | Tianyi Zhou | Chengqi Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Wrong labeling problem and long-tail relations are two main challenges caused by distant supervision in relation extraction. Recent works alleviate the wrong labeling by selective attention via multi-instance learning, but cannot well handle long-tail relations even if hierarchies of the relations are introduced to share knowledge. In this work, we propose a novel neural network, Collaborating Relation-augmented Attention (CoRA), to handle both the wrong labeling and long-tail relations. Particularly, we first propose relation-augmented attention network as base model. It operates on sentence bag with a sentence-to-relation attention to minimize the effect of wrong labeling. Then, facilitated by the proposed base model, we introduce collaborating relation features shared among relations in the hierarchies to promote the relation-augmenting process and balance the training data for long-tail relations. Besides the main training objective to predict the relation of a sentence bag, an auxiliary objective is utilized to guide the relation-augmenting process for a more accurate bag-level representation. In the experiments on the popular benchmark dataset NYT, the proposed CoRA improves the prior state-of-the-art performance by a large margin in terms of Precision@N, AUC and Hits@K. Further analyses verify its superior capability in handling long-tail relations in contrast to the competitors.

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Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases
Yunshi Lan | Jing Jiang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Previous work on answering complex questions from knowledge bases usually separately addresses two types of complexity: questions with constraints and questions with multiple hops of relations. In this paper, we handle both types of complexity at the same time. Motivated by the observation that early incorporation of constraints into query graphs can more effectively prune the search space, we propose a modified staged query graph generation method with more flexible ways to generate query graphs. Our experiments clearly show that our method achieves the state of the art on three benchmark KBQA datasets.

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Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer
Jianfei Yu | Jing Jiang | Li Yang | Rui Xia
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In this paper, we study Multimodal Named Entity Recognition (MNER) for social media posts. Existing approaches for MNER mainly suffer from two drawbacks: (1) despite generating word-aware visual representations, their word representations are insensitive to the visual context; (2) most of them ignore the bias brought by the visual context. To tackle the first issue, we propose a multimodal interaction module to obtain both image-aware word representations and word-aware visual representations. To alleviate the visual bias, we further propose to leverage purely text-based entity span detection as an auxiliary module, and design a Unified Multimodal Transformer to guide the final predictions with the entity span predictions. Experiments show that our unified approach achieves the new state-of-the-art performance on two benchmark datasets.

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Cross-Thought for Sentence Encoder Pre-training
Shuohang Wang | Yuwei Fang | Siqi Sun | Zhe Gan | Yu Cheng | Jingjing Liu | Jing Jiang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering. Instead of using the original signals of full sentences, we train a Transformer-based sequence encoder over a large set of short sequences, which allows the model to automatically select the most useful information for predicting masked words. Experiments on question answering and textual entailment tasks demonstrate that our pre-trained encoder can outperform state-of-the-art encoders trained with continuous sentence signals as well as traditional masked language modeling baselines. Our proposed approach also achieves new state of the art on HotpotQA (full-wiki setting) by improving intermediate information retrieval performance.

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Coupled Hierarchical Transformer for Stance-Aware Rumor Verification in Social Media Conversations
Jianfei Yu | Jing Jiang | Ling Min Serena Khoo | Hai Leong Chieu | Rui Xia
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The prevalent use of social media enables rapid spread of rumors on a massive scale, which leads to the emerging need of automatic rumor verification (RV). A number of previous studies focus on leveraging stance classification to enhance RV with multi-task learning (MTL) methods. However, most of these methods failed to employ pre-trained contextualized embeddings such as BERT, and did not exploit inter-task dependencies by using predicted stance labels to improve the RV task. Therefore, in this paper, to extend BERT to obtain thread representations, we first propose a Hierarchical Transformer, which divides each long thread into shorter subthreads, and employs BERT to separately represent each subthread, followed by a global Transformer layer to encode all the subthreads. We further propose a Coupled Transformer Module to capture the inter-task interactions and a Post-Level Attention layer to use the predicted stance labels for RV, respectively. Experiments on two benchmark datasets show the superiority of our Coupled Hierarchical Transformer model over existing MTL approaches.

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Two-Headed Monster and Crossed Co-Attention Networks
Yaoyiran Li | Jing Jiang
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: Student Research Workshop

This paper investigates a new co-attention mechanism in neural transduction models for machine translation tasks. We propose a paradigm, termed Two-Headed Monster (THM), which consists of two symmetric encoder modules and one decoder module connected with co-attention. As a specific and concrete implementation of THM, Crossed Co-Attention Networks (CCNs) are designed based on the Transformer model. We test CCNs on WMT 2014 EN-DE and WMT 2016 EN-FI translation tasks and show both advantages and disadvantages of the proposed method. Our model outperforms the strong Transformer baseline by 0.51 (big) and 0.74 (base) BLEU points on EN-DE and by 0.17 (big) and 0.47 (base) BLEU points on EN-FI but the epoch time increases by circa 75%.

2019

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Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Kentaro Inui | Jing Jiang | Vincent Ng | Xiaojun Wan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

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Unsupervised Deep Structured Semantic Models for Commonsense Reasoning
Shuohang Wang | Sheng Zhang | Yelong Shen | Xiaodong Liu | Jingjing Liu | Jianfeng Gao | Jing Jiang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.

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Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together
Tao Shen | Tianyi Zhou | Guodong Long | Jing Jiang | Chengqi Zhang
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies. Further, their expressive power and performance can be boosted by using a vector to measure pairwise dependency, but this requires to expand the alignment matrix to a tensor, which results in memory and computation bottlenecks. In this paper, we propose a novel attention mechanism called “Multi-mask Tensorized Self-Attention” (MTSA), which is as fast and as memory-efficient as a CNN, but significantly outperforms previous CNN-/RNN-/attention-based models. MTSA 1) captures both pairwise (token2token) and global (source2token) dependencies by a novel compatibility function composed of dot-product and additive attentions, 2) uses a tensor to represent the feature-wise alignment scores for better expressive power but only requires parallelizable matrix multiplications, and 3) combines multi-head with multi-dimensional attentions, and applies a distinct positional mask to each head (subspace), so the memory and computation can be distributed to multiple heads, each with sequential information encoded independently. The experiments show that a CNN/RNN-free model based on MTSA achieves state-of-the-art or competitive performance on nine NLP benchmarks with compelling memory- and time-efficiency.

2018

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Embedding WordNet Knowledge for Textual Entailment
Yunshi Lan | Jing Jiang
Proceedings of the 27th International Conference on Computational Linguistics

In this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet. Our idea is to embed the lexical entailment knowledge contained in WordNet in specially-learned word vectors, which we call “entailment vectors.” We present a standard neural network model and a novel set-theoretic model to learn these entailment vectors from word pairs with known lexical entailment relations derived from WordNet. We further incorporate these entailment vectors into a decomposable attention model for textual entailment and evaluate the model on the SICK and the SNLI dataset. We find that using these special entailment word vectors, we can significantly improve the performance of textual entailment compared with a baseline that uses only standard word2vec vectors. The final performance of our model is close to or above the state of the art, but our method does not rely on any manually-crafted rules or extensive syntactic features.

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A Co-Matching Model for Multi-choice Reading Comprehension
Shuohang Wang | Mo Yu | Jing Jiang | Shiyu Chang
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Multi-choice reading comprehension is a challenging task, which involves the matching between a passage and a question-answer pair. This paper proposes a new co-matching approach to this problem, which jointly models whether a passage can match both a question and a candidate answer. Experimental results on the RACE dataset demonstrate that our approach achieves state-of-the-art performance.

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Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network
Jianfei Yu | Luís Marujo | Jing Jiang | Pradeep Karuturi | William Brendel
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method.

2017

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Can Syntax Help? Improving an LSTM-based Sentence Compression Model for New Domains
Liangguo Wang | Jing Jiang | Hai Leong Chieu | Chen Hui Ong | Dandan Song | Lejian Liao
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we study how to improve the domain adaptability of a deletion-based Long Short-Term Memory (LSTM) neural network model for sentence compression. We hypothesize that syntactic information helps in making such models more robust across domains. We propose two major changes to the model: using explicit syntactic features and introducing syntactic constraints through Integer Linear Programming (ILP). Our evaluation shows that the proposed model works better than the original model as well as a traditional non-neural-network-based model in a cross-domain setting.

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Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification
Jianfei Yu | Jing Jiang
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In this paper, we study domain adaptation with a state-of-the-art hierarchical neural network for document-level sentiment classification. We first design a new auxiliary task based on sentiment scores of domain-independent words. We then propose two neural network architectures to respectively induce document embeddings and sentence embeddings that work well for different domains. When these document and sentence embeddings are used for sentiment classification, we find that with both pseudo and external sentiment lexicons, our proposed methods can perform similarly to or better than several highly competitive domain adaptation methods on a benchmark dataset of product reviews.

2016

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Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification
Jianfei Yu | Jing Jiang
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Learning Natural Language Inference with LSTM
Shuohang Wang | Jing Jiang
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Pairwise Relation Classification with Mirror Instances and a Combined Convolutional Neural Network
Jianfei Yu | Jing Jiang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Relation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especially in order to better handle the asymmetry of relation types, in this paper, we propose a neural network based method for relation classification that combines the raw sequence and the shortest dependency path representations of relation instances and uses mirror instances to perform pairwise relation classification. We evaluate our proposed models on the SemEval-2010 Task 8 dataset. The empirical results show that with two additional features, our model achieves the state-of-the-art result of F1 score of 85.7.

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Hashtag Recommendation with Topical Attention-Based LSTM
Yang Li | Ting Liu | Jing Jiang | Liang Zhang
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Microblogging services allow users to create hashtags to categorize their posts. In recent years, the task of recommending hashtags for microblogs has been given increasing attention. However, most of existing methods depend on hand-crafted features. Motivated by the successful use of long short-term memory (LSTM) for many natural language processing tasks, in this paper, we adopt LSTM to learn the representation of a microblog post. Observing that hashtags indicate the primary topics of microblog posts, we propose a novel attention-based LSTM model which incorporates topic modeling into the LSTM architecture through an attention mechanism. We evaluate our model using a large real-world dataset. Experimental results show that our model significantly outperforms various competitive baseline methods. Furthermore, the incorporation of topical attention mechanism gives more than 7.4% improvement in F1 score compared with standard LSTM method.

2015

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A Joint Model of Product Properties, Aspects and Ratings for Online Reviews
Ying Ding | Jing Jiang
Proceedings of the International Conference Recent Advances in Natural Language Processing

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Towards Opinion Summarization from Online Forums
Ying Ding | Jing Jiang
Proceedings of the International Conference Recent Advances in Natural Language Processing

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A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features
Jianfei Yu | Jing Jiang
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)

2014

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A Unified Topic-Style Model for Online Discussions
Ying Ding | Jing Jiang | Qiming Diao
Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media

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Generating Supplementary Travel Guides from Social Media
Liu Yang | Jing Jiang | Lifu Huang | Minghui Qiu | Lizi Liao
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

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Learning Topics and Positions from Debatepedia
Swapna Gottipati | Minghui Qiu | Yanchuan Sim | Jing Jiang | Noah A. Smith
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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A Unified Model for Topics, Events and Users on Twitter
Qiming Diao | Jing Jiang
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Mining User Relations from Online Discussions using Sentiment Analysis and Probabilistic Matrix Factorization
Minghui Qiu | Liu Yang | Jing Jiang
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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A Latent Variable Model for Viewpoint Discovery from Threaded Forum Posts
Minghui Qiu | Jing Jiang
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Joint Learning for Coreference Resolution with Markov Logic
Yang Song | Jing Jiang | Wayne Xin Zhao | Sujian Li | Houfeng Wang
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Identifying Event-related Bursts via Social Media Activities
Xin Zhao | Baihan Shu | Jing Jiang | Yang Song | Hongfei Yan | Xiaoming Li
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Finding Thoughtful Comments from Social Media
Swapna Gottipati | Jing Jiang
Proceedings of COLING 2012

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Extracting and Normalizing Entity-Actions from Users’ Comments
Swapna Gottipati | Jing Jiang
Proceedings of COLING 2012: Posters

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Finding Bursty Topics from Microblogs
Qiming Diao | Jing Jiang | Feida Zhu | Ee-Peng Lim
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2011

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Link Type Based Pre-Cluster Pair Model for Coreference Resolution
Yang Song | Houfeng Wang | Jing Jiang
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

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Linking Entities to a Knowledge Base with Query Expansion
Swapna Gottipati | Jing Jiang
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Unsupervised Information Extraction with Distributional Prior Knowledge
Cane Wing-ki Leung | Jing Jiang | Kian Ming A. Chai | Hai Leong Chieu | Loo-Nin Teow
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Generating Aspect-oriented Multi-Document Summarization with Event-aspect model
Peng Li | Yinglin Wang | Wei Gao | Jing Jiang
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Extracting Relation Descriptors with Conditional Random Fields
Yaliang Li | Jing Jiang | Hai Leong Chieu | Kian Ming A. Chai
Proceedings of 5th International Joint Conference on Natural Language Processing

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Topical Keyphrase Extraction from Twitter
Xin Zhao | Jing Jiang | Jing He | Yang Song | Palakorn Achanauparp | Ee-Peng Lim | Xiaoming Li
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Generating Templates of Entity Summaries with an Entity-Aspect Model and Pattern Mining
Peng Li | Jing Jiang | Yinglin Wang
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid
Xin Zhao | Jing Jiang | Hongfei Yan | Xiaoming Li
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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Multi-Task Transfer Learning for Weakly-Supervised Relation Extraction
Jing Jiang
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

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Extracting Paraphrases of Technical Terms from Noisy Parallel Software Corpora
Xiaoyin Wang | David Lo | Jing Jiang | Lu Zhang | Hong Mei
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

2007

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A Systematic Exploration of the Feature Space for Relation Extraction
Jing Jiang | ChengXiang Zhai
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Instance Weighting for Domain Adaptation in NLP
Jing Jiang | ChengXiang Zhai
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Exploiting Domain Structure for Named Entity Recognition
Jing Jiang | ChengXiang Zhai
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference