Emotion Cause Extraction: A Question Answering Approach

A Question Answering Approach to Emotion Cause Extraction Lin Gui, Jiannan Hu, Yulan He, Ruifeng Xu*, Qin Lu, Jiachen Du

Outlines Introduction Related Works Our Approach Experiments and Evaluation Conclusions and Future Works

Introduction Previous researches on emotion recognition in text usually focus on classification and elements (feeler, expression et al.) extraction. But there are few works on extracting the reason behind emotional expressions. Document: (I lost my phone yesterday, and I feel sad now. ) Emotion Sad Emotional Expression: Emotion Cause:

Related Works There is a few researches on emotion cause detection: Sophia M. Y. Lee Rule-based Emotion Cause(COLING 2010) Gao OCC-model based Emotion Cause Detection(Expert system with applications 2015) Ghazi CRF-based Emotion Cause Detection (CICLING 2015) But these works didn t release public datasets for researching

Related Works Gui et al firstly released a manually annotated dataset for emotion cause extraction on EMNLP 2016. We collected more than 20,000 news from Sina social news and selected 2105 documents with one emotional expression and at least one emotion causes . All the emotional keywords and causes are annotated by W3C emotional markup language <instance id= 29 > <clause id= 5 cause= N keywords= N > <text> </text> </clause> <clause id= 6 cause= N keywords= N > <text> (</text> </clause> <clause id= 7 cause= Y keywords= N > <text> (</text> <cause id= 1 type= v begin= 0 length= 7 > </cause> </clause> <clause id= 8 cause= N keywords= Y > <text> (</text> <keywords emotion= happiness keywords-begin= 5 keywords-length= 2 > </keywords> </clause> </instance>

Related Works [Gui et al. 2016] used dependency parser to parse the text to tree structures. The emotion cause extraction task was converted to tree classification task. Then two variants of Tree-Kernel SVMs were used to classify the tress. This model heavily depends on accurate dependency trees. And also this model cannot extract phrase-level emotion causes.

Our Approach Emotion Cause Detection is analogue to Question Answering Emotional Text as Reading Text Emotional Words as Question Emotion Cause as Answer

Memory Network (Memnet) To model the process of question answering, we use Memory Networks [Sukhbaatar et al. 2015] as our base model. And the multiple-hop was used to better represent the text. One-Hop Architecture Multiple-Hop Architecture

Convolutional Memory Network (Conv-Memnet) In order to capture context information for clauses, we propose a new architecture which cotain more memory slot to model the context with a convolutional operation. the weight of word wiin the i-th position considers both the previous word wi1 and the fol-lowing word wi+1 by a convolutional operation:

Experiments and Evaluation We conduct experiments on a simplified Chinese emotion cause corpus (Gui et al., 2016) , the only publicly available dataset on this task to the best of our knowledge. It is commonly accepted so that we can compare our results with others. If a proposed emotion cause clause covers the annotated answer, the word sequence is considered correct. The precision, recall, and F-measure are defined by Item Number Documents 2,105 Clauses 11,799 Emotion Causes 2,167 Documents with 1 emotions 2,046 Documents with 2 emotions 56 Documents with 3 emotions 3 Available at: http://hlt.hitsz.edu.cn/?page id=694

Experiments and Evaluation Performance compared with baselines RB (Rule based method): The rule based method proposed in (Lee et al., 2010) CB (Common-sense based method): We use the Chinese Emotion Cognition Lexicon (Xu et al., 2013) as the common-sense knowledge base. RB+CB+ML (Machine learning method trained from rule-based features and facts from a common-sense knowledge base): This methods was previously proposed for emotion cause classification in (Chen et al., 2010). SVM: This is a SVM classifier using the unigram, bigram and trigram features Word2vec: This is a SVM classifier using word representations learned by Word2vec (Mikolov et al., 2013) as features. Multi-kernel: This is the state-of-the-art method using the multi-kernel method (Gui et al., 2016) to identify the emotion cause. We use the best performance reported in their paper. CNN: The convolutional neural network for sentence classification (Kim, 2014).

Results Performance compared with baselines

Results Key phrases extracted from attention mechanism

Results In order to evaluate the quality of keywords extracted by memory networks, we define a new metric on the keyword level of emotion cause extraction. The keyword is defined as the word which obtains the highest attention weight in the identified clause. If the keywords extracted by our algorithm is located within the boundary of annotation, it is treated as correct.

Conclusion We proposed a novel QA based framework for emotion cause extraction A new convolutional operation was introduced to memory networks Thoughtful experiments showed efficiency of our method

Appendix To better work on emotion cause extraction, we organized NTCIR emotion cause extraction shared task . We annotated more documents in both English and Chinese for this task. The data will be released as soon as possible.