Shoushan Li


2021

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More than Text: Multi-modal Chinese Word Segmentation
Dong Zhang | Zheng Hu | Shoushan Li | Hanqian Wu | Qiaoming Zhu | Guodong Zhou
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)

Chinese word segmentation (CWS) is undoubtedly an important basic task in natural language processing. Previous works only focus on the textual modality, but there are often audio and video utterances (such as news broadcast and face-to-face dialogues), where textual, acoustic and visual modalities normally exist. To this end, we attempt to combine the multi-modality (mainly the converted text and actual voice information) to perform CWS. In this paper, we annotate a new dataset for CWS containing text and audio. Moreover, we propose a time-dependent multi-modal interactive model based on Transformer framework to integrate multi-modal information for word sequence labeling. The experimental results on three different training sets show the effectiveness of our approach with fusing text and audio.

2020

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End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network
Ying Chen | Wenjun Hou | Shoushan Li | Caicong Wu | Xiaoqiang Zhang
Proceedings of the 28th International Conference on Computational Linguistics

Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages. Considering that most emotions usually have few causes mentioned in their contexts, we present a novel end-to-end Pair Graph Convolutional Network (PairGCN) to model pair-level contexts so that to capture the dependency information among local neighborhood candidate pairs. Moreover, in the graphical network, contexts are grouped into three types and each type of contexts is propagated by its own way. Experiments on a benchmark Chinese emotion-cause pair extraction corpus demonstrate the effectiveness of the proposed model.

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Multimodal Topic-Enriched Auxiliary Learning for Depression Detection
Minghui An | Jingjing Wang | Shoushan Li | Guodong Zhou
Proceedings of the 28th International Conference on Computational Linguistics

From the perspective of health psychology, human beings with long-term and sustained negativity are highly possible to be diagnosed with depression. Inspired by this, we argue that the global topic information derived from user-generated contents (e.g., texts and images) is crucial to boost the performance of the depression detection task, though this information has been neglected by almost all previous studies on depression detection. To this end, we propose a new Multimodal Topic-enriched Auxiliary Learning (MTAL) approach, aiming at capturing the topic information inside different modalities (i.e., texts and images) for depression detection. Especially, in our approach, a modality-agnostic topic model is proposed to be capable of mining the topical clues from either the discrete textual signals or the continuous visual signals. On this basis, the topic modeling w.r.t. the two modalities are cast as two auxiliary tasks for improving the performance of the primary task (i.e., depression detection). Finally, the detailed evaluation demonstrates the great advantage of our MTAL approach to depression detection over the state-of-the-art baselines. This justifies the importance of the multimodal topic information to depression detection and the effectiveness of our approach in capturing such information.

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Sentiment Forecasting in Dialog
Zhongqing Wang | Xiujun Zhu | Yue Zhang | Shoushan Li | Guodong Zhou
Proceedings of the 28th International Conference on Computational Linguistics

Sentiment forecasting in dialog aims to predict the polarity of next utterance to come, and can help speakers revise their utterances in sentimental utterances generation. However, the polarity of next utterance is normally hard to predict, due to the lack of content of next utterance (yet to come). In this study, we propose a Neural Sentiment Forecasting (NSF) model to address inherent challenges. In particular, we employ a neural simulation model to simulate the next utterance based on the context (previous utterances encountered). Moreover, we employ a sequence influence model to learn both pair-wise and seq-wise influence. Empirical studies illustrate the importance of proposed sentiment forecasting task, and justify the effectiveness of our NSF model over several strong baselines.

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Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue
WeiSheng Zhang | Kaisong Song | Yangyang Kang | Zhongqing Wang | Changlong Sun | Xiaozhong Liu | Shoushan Li | Min Zhang | Luo Si
Findings of the Association for Computational Linguistics: EMNLP 2020

As an important research topic, customer service dialogue generation tends to generate generic seller responses by leveraging current dialogue information. In this study, we propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information, which can be both accessible and informative. By utilizing innovative historical dialogue representation learning and historical dialogue selection mechanism, the proposed model is capable of detecting most related responses from sellers’ historical dialogues, which can further enhance the current dialogue generation quality. Unlike prior dialogue generation efforts, we treat each seller’s historical dialogues as a list of Customer-Seller utterance pairs and allow the model to measure their different importance, and copy words directly from most relevant pairs. Extensive experimental results show that the proposed approach can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset.

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Aspect Sentiment Classification with Document-level Sentiment Preference Modeling
Xiao Chen | Changlong Sun | Jingjing Wang | Shoushan Li | Luo Si | Min Zhang | Guodong Zhou
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

In the literature, existing studies always consider Aspect Sentiment Classification (ASC) as an independent sentence-level classification problem aspect by aspect, which largely ignore the document-level sentiment preference information, though obviously such information is crucial for alleviating the information deficiency problem in ASC. In this paper, we explore two kinds of sentiment preference information inside a document, i.e., contextual sentiment consistency w.r.t. the same aspect (namely intra-aspect sentiment consistency) and contextual sentiment tendency w.r.t. all the related aspects (namely inter-aspect sentiment tendency). On the basis, we propose a Cooperative Graph Attention Networks (CoGAN) approach for cooperatively learning the aspect-related sentence representation. Specifically, two graph attention networks are leveraged to model above two kinds of document-level sentiment preference information respectively, followed by an interactive mechanism to integrate the two-fold preference. Detailed evaluation demonstrates the great advantage of the proposed approach to ASC over the state-of-the-art baselines. This justifies the importance of the document-level sentiment preference information to ASC and the effectiveness of our approach capturing such information.

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Multi-modal Multi-label Emotion Detection with Modality and Label Dependence
Dong Zhang | Xincheng Ju | Junhui Li | Shoushan Li | Qiaoming Zhu | Guodong Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

As an important research issue in the natural language processing community, multi-label emotion detection has been drawing more and more attention in the last few years. However, almost all existing studies focus on one modality (e.g., textual modality). In this paper, we focus on multi-label emotion detection in a multi-modal scenario. In this scenario, we need to consider both the dependence among different labels (label dependence) and the dependence between each predicting label and different modalities (modality dependence). Particularly, we propose a multi-modal sequence-to-set approach to effectively model both kinds of dependence in multi-modal multi-label emotion detection. The detailed evaluation demonstrates the effectiveness of our approach.

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基于对话约束的回复生成研究(Research on Response Generation via Dialogue Constraints)
Mengyu Guan (管梦雨) | Zhongqing Wang (王中卿) | Shoushan Li (李寿山) | Guodong Zhou (周国栋)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

现有的对话系统中存在着生成“好的”、“我不知道”等无意义的安全回复问题。日常对话中,对话者通常围绕特定的主题进行讨论且每句话都有明显的情感和意图。因此该文提出了基于对话约束的回复生成模型,即在Seq2Seq模型的基础上,结合对对话的主题、情感、意图的识别。该方法对生成回复的主题、情感和意图进行约束,从而生成具有合理的情感和意图且与对话主题相关的回复。实验证明,该文提出的方法能有效地提高生成回复的质量。

2019

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Emotion Detection with Neural Personal Discrimination
Xiabing Zhou | Zhongqing Wang | Shoushan Li | Guodong Zhou | Min Zhang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

There have been a recent line of works to automatically predict the emotions of posts in social media. Existing approaches consider the posts individually and predict their emotions independently. Different from previous researches, we explore the dependence among relevant posts via the authors’ backgrounds, since the authors with similar backgrounds, e.g., gender, location, tend to express similar emotions. However, such personal attributes are not easy to obtain in most social media websites, and it is hard to capture attributes-aware words to connect similar people. Accordingly, we propose a Neural Personal Discrimination (NPD) approach to address above challenges by determining personal attributes from posts, and connecting relevant posts with similar attributes to jointly learn their emotions. In particular, we employ adversarial discriminators to determine the personal attributes, with attention mechanisms to aggregate attributes-aware words. In this way, social correlationship among different posts can be better addressed. Experimental results show the usefulness of personal attributes, and the effectiveness of our proposed NPD approach in capturing such personal attributes with significant gains over the state-of-the-art models.

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Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning
Jingjing Wang | Changlong Sun | Shoushan Li | Jiancheng Wang | Luo Si | Min Zhang | Xiaozhong Liu | Guodong Zhou
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recently, neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC). However, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. In this paper, to simulating the steps of analyzing aspect sentiment in a document by human beings, we propose a new Hierarchical Reinforcement Learning (HRL) approach to DASC. This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC. First, a high-level policy is proposed to select aspect-relevant clauses and discard noisy clauses. Then, a low-level policy is proposed to select sentiment-relevant words and discard noisy words inside the selected clauses. Finally, a sentiment rating predictor is designed to provide reward signals to guide both clause and word selection. Experimental results demonstrate the impressive effectiveness of the proposed approach to DASC over the state-of-the-art baselines.

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Adversarial Attention Modeling for Multi-dimensional Emotion Regression
Suyang Zhu | Shoushan Li | Guodong Zhou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this paper, we propose a neural network-based approach, namely Adversarial Attention Network, to the task of multi-dimensional emotion regression, which automatically rates multiple emotion dimension scores for an input text. Especially, to determine which words are valuable for a particular emotion dimension, an attention layer is trained to weight the words in an input sequence. Furthermore, adversarial training is employed between two attention layers to learn better word weights via a discriminator. In particular, a shared attention layer is incorporated to learn public word weights between two emotion dimensions. Empirical evaluation on the EMOBANK corpus shows that our approach achieves notable improvements in r-values on both EMOBANK Reader’s and Writer’s multi-dimensional emotion regression tasks in all domains over the state-of-the-art baselines.

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Aspect Sentiment Classification Towards Question-Answering with Reinforced Bidirectional Attention Network
Jingjing Wang | Changlong Sun | Shoushan Li | Xiaozhong Liu | Luo Si | Min Zhang | Guodong Zhou
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In the literature, existing studies on aspect sentiment classification (ASC) focus on individual non-interactive reviews. This paper extends the research to interactive reviews and proposes a new research task, namely Aspect Sentiment Classification towards Question-Answering (ASC-QA), for real-world applications. This new task aims to predict sentiment polarities for specific aspects from interactive QA style reviews. In particular, a high-quality annotated corpus is constructed for ASC-QA to facilitate corresponding research. On this basis, a Reinforced Bidirectional Attention Network (RBAN) approach is proposed to address two inherent challenges in ASC-QA, i.e., semantic matching between question and answer, and data noise. Experimental results demonstrate the great advantage of the proposed approach to ASC-QA against several state-of-the-art baselines.

2018

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Cross-media User Profiling with Joint Textual and Social User Embedding
Jingjing Wang | Shoushan Li | Mingqi Jiang | Hanqian Wu | Guodong Zhou
Proceedings of the 27th International Conference on Computational Linguistics

In realistic scenarios, a user profiling model (e.g., gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to the different data distributions in the two media. In this paper, we address cross-media user profiling by bridging the knowledge between the source and target media with a uniform user embedding learning approach. In our approach, we first construct a cross-media user-word network to capture the relationship among users through the textual information and a modified cross-media user-user network to capture the relationship among users through the social information. Then, we learn user embedding by jointly learning the heterogeneous network composed of above two networks. Finally, we train a classification (or regression) model with the obtained user embeddings as input to perform user profiling. Empirical studies demonstrate the effectiveness of the proposed approach to two cross-media user profiling tasks, i.e., cross-media gender classification and cross-media age regression.

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One vs. Many QA Matching with both Word-level and Sentence-level Attention Network
Lu Wang | Shoushan Li | Changlong Sun | Luo Si | Xiaozhong Liu | Min Zhang | Guodong Zhou
Proceedings of the 27th International Conference on Computational Linguistics

Question-Answer (QA) matching is a fundamental task in the Natural Language Processing community. In this paper, we first build a novel QA matching corpus with informal text which is collected from a product reviewing website. Then, we propose a novel QA matching approach, namely One vs. Many Matching, which aims to address the novel scenario where one question sentence often has an answer with multiple sentences. Furthermore, we improve our matching approach by employing both word-level and sentence-level attentions for solving the noisy problem in the informal text. Empirical studies demonstrate the effectiveness of the proposed approach to question-answer matching.

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Joint Learning for Emotion Classification and Emotion Cause Detection
Ying Chen | Wenjun Hou | Xiyao Cheng | Shoushan Li
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present a neural network-based joint approach for emotion classification and emotion cause detection, which attempts to capture mutual benefits across the two sub-tasks of emotion analysis. Considering that emotion classification and emotion cause detection need different kinds of features (affective and event-based separately), we propose a joint encoder which uses a unified framework to extract features for both sub-tasks and a joint model trainer which simultaneously learns two models for the two sub-tasks separately. Our experiments on Chinese microblogs show that the joint approach is very promising.

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Sentiment Classification towards Question-Answering with Hierarchical Matching Network
Chenlin Shen | Changlong Sun | Jingjing Wang | Yangyang Kang | Shoushan Li | Xiaozhong Liu | Luo Si | Min Zhang | Guodong Zhou
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In an e-commerce environment, user-oriented question-answering (QA) text pair could carry rich sentiment information. In this study, we propose a novel task/method to address QA sentiment analysis. In particular, we create a high-quality annotated corpus with specially-designed annotation guidelines for QA-style sentiment classification. On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair. First, we segment both the question and answer text into sentences and construct a number of [Q-sentence, A-sentence] units in each QA text pair. Then, by leveraging a QA bidirectional matching layer, the proposed approach can learn the matching vectors of each [Q-sentence, A-sentence] unit. Finally, we characterize the importance of the generated matching vectors via a self-matching attention layer. Experimental results, comparing with a number of state-of-the-art baselines, demonstrate the impressive effectiveness of the proposed approach for QA-style sentiment classification.

2016

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A Bilingual Attention Network for Code-switched Emotion Prediction
Zhongqing Wang | Yue Zhang | Sophia Lee | Shoushan Li | Guodong Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Emotions in code-switching text can be expressed in either monolingual or bilingual forms. However, relatively little research has emphasized on code-switching text. In this paper, we propose a Bilingual Attention Network (BAN) model to aggregate the monolingual and bilingual informative words to form vectors from the document representation, and integrate the attention vectors to predict the emotion. The experiments show that the effectiveness of the proposed model. Visualization of the attention layers illustrates that the model selects qualitatively informative words.

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Semi-supervised Gender Classification with Joint Textual and Social Modeling
Shoushan Li | Bin Dai | Zhengxian Gong | Guodong Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In gender classification, labeled data is often limited while unlabeled data is ample. This motivates semi-supervised learning for gender classification to improve the performance by exploring the knowledge in both labeled and unlabeled data. In this paper, we propose a semi-supervised approach to gender classification by leveraging textual features and a specific kind of indirect links among the users which we call “same-interest” links. Specifically, we propose a factor graph, namely Textual and Social Factor Graph (TSFG), to model both the textual and the “same-interest” link information. Empirical studies demonstrate the effectiveness of the proposed approach to semi-supervised gender classification.

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User Classification with Multiple Textual Perspectives
Dong Zhang | Shoushan Li | Hongling Wang | Guodong Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Textual information is of critical importance for automatic user classification in social media. However, most previous studies model textual features in a single perspective while the text in a user homepage typically possesses different styles of text, such as original message and comment from others. In this paper, we propose a novel approach, namely ensemble LSTM, to user classification by incorporating multiple textual perspectives. Specifically, our approach first learns a LSTM representation with a LSTM recurrent neural network and then presents a joint learning method to integrating all naturally-divided textual perspectives. Empirical studies on two basic user classification tasks, i.e., gender classification and age classification, demonstrate the effectiveness of the proposed approach to user classification with multiple textual perspectives.

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Two-View Label Propagation to Semi-supervised Reader Emotion Classification
Shoushan Li | Jian Xu | Dong Zhang | Guodong Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In the literature, various supervised learning approaches have been adopted to address the task of reader emotion classification. However, the classification performance greatly suffers when the size of the labeled data is limited. In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm. Specifically, our approach depends on two word-document bipartite graphs to model the relationship among the samples in the two views respectively. Besides, the two bipartite graphs are integrated by linking each source text sample with its corresponding response text sample via a length-sensitive transition probability. In this way, our two-view label propagation approach to semi-supervised reader emotion classification largely alleviates the reliance on the strong sufficiency and independence assumptions of the two views, as required in co-training. Empirical evaluation demonstrates the effectiveness of our two-view label propagation approach to semi-supervised reader emotion classification.

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Corpus Fusion for Emotion Classification
Suyang Zhu | Shoushan Li | Ying Chen | Guodong Zhou
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Machine learning-based methods have obtained great progress on emotion classification. However, in most previous studies, the models are learned based on a single corpus which often suffers from insufficient labeled data. In this paper, we propose a corpus fusion approach to address emotion classification across two corpora which use different emotion taxonomies. The objective of this approach is to utilize the annotated data from one corpus to help the emotion classification on another corpus. An Integer Linear Programming (ILP) optimization is proposed to refine the classification results. Empirical studies show the effectiveness of the proposed approach to corpus fusion for emotion classification.

2015

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Sentence-level Emotion Classification with Label and Context Dependence
Shoushan Li | Lei Huang | Rong Wang | Guodong Zhou
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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Semi-Stacking for Semi-supervised Sentiment Classification
Shoushan Li | Lei Huang | Jingjing Wang | Guodong Zhou
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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Emotion Detection in Code-switching Texts via Bilingual and Sentimental Information
Zhongqing Wang | Sophia Lee | Shoushan Li | Guodong Zhou
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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Skill Inference with Personal and Skill Connections
Zhongqing Wang | Shoushan Li | Hanxiao Shi | Guodong Zhou
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Annotating Events in an Emotion Corpus
Sophia Lee | Shoushan Li | Chu-Ren Huang
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper presents the development of a Chinese event-based emotion corpus. It specifically describes the corpus design, collection and annotation. The proposed annotation scheme provides a consistent way of identifying some emotion-associated events (namely pre-events and post-events). Corpus data show that there are significant interactions between emotions and pre-events as well as that of between emotion and post-events. We believe that emotion as a pivot event underlies an innovative approach towards a linguistic model of emotion as well as automatic emotion detection and classification.

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Bilingual Event Extraction: a Case Study on Trigger Type Determination
Zhu Zhu | Shoushan Li | Guodong Zhou | Rui Xia
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Collective Personal Profile Summarization with Social Networks
Zhongqing Wang | Shoushan Li | Fang Kong | Guodong Zhou
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Joint Modeling of News Reader’s and Comment Writer’s Emotions
Huanhuan Liu | Shoushan Li | Guodong Zhou | Chu-Ren Huang | Peifeng Li
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Dual Training and Dual Prediction for Polarity Classification
Rui Xia | Tao Wang | Xuelei Hu | Shoushan Li | Chengqing Zong
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Active Learning for Imbalanced Sentiment Classification
Shoushan Li | Shengfeng Ju | Guodong Zhou | Xiaojun Li
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Soochow University Word Segmenter for SIGHAN 2012 Bakeoff
Yan Fang | Zhongqing Wang | Shoushan Li | Zhongguo Li | Richen Xu | Leixin Cai
Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing

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Active Learning for Chinese Word Segmentation
Shoushan Li | Guodong Zhou | Chu-Ren Huang
Proceedings of COLING 2012: Posters

2010

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Employing Personal/Impersonal Views in Supervised and Semi-Supervised Sentiment Classification
Shoushan Li | Chu-Ren Huang | Guodong Zhou | Sophia Yat Mei Lee
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Emotion Cause Events: Corpus Construction and Analysis
Sophia Yat Mei Lee | Ying Chen | Shoushan Li | Chu-Ren Huang
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Emotion processing has always been a great challenge. Given the fact that an emotion is triggered by cause events and that cause events are an integral part of emotion, this paper constructs a Chinese emotion cause corpus as a first step towards automatic inference of cause-emotion correlation. The corpus focuses on five primary emotions, namely happiness, sadness, fear, anger, and surprise. It is annotated with emotion cause events based on our proposed annotation scheme. Corpus data shows that most emotions are expressed with causes, and that causes mostly occur before the corresponding emotion verbs. We also examine the correlations between emotions and cause events in terms of linguistic cues: causative verbs, perception verbs, epistemic markers, conjunctions, prepositions, and others. Results show that each group of linguistic cues serves as an indicator marking the cause events in different structures of emotional constructions. We believe that the emotion cause corpus will be the useful resource for automatic emotion cause detection as well as emotion detection and classification.

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Emotion Cause Detection with Linguistic Constructions
Ying Chen | Sophia Yat Mei Lee | Shoushan Li | Chu-Ren Huang
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Sentiment Classification and Polarity Shifting
Shoushan Li | Sophia Y. M. Lee | Ying Chen | Chu-Ren Huang | Guodong Zhou
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2009

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A Framework of Feature Selection Methods for Text Categorization
Shoushan Li | Rui Xia | Chengqing Zong | Chu-Ren Huang
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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Sentiment Classification Considering Negation and Contrast Transition
Shoushan Li | Chu-Ren Huang
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1

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Word Boundary Decision with CRF for Chinese Word Segmentation
Shoushan Li | Chu-Ren Huang
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

2008

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Sentence Type Based Reordering Model for Statistical Machine Translation
Jiajun Zhang | Chengqing Zong | Shoushan Li
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

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Multi-domain Sentiment Classification
Shoushan Li | Chengqing Zong
Proceedings of ACL-08: HLT, Short Papers