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Chew Lim Tan

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5 papers

1 author row

AAAI Conference 2013 Conference Paper

From Semantic to Emotional Space in Probabilistic Sense Sentiment Analysis

Mitra Mohtarami
Man Lan
Chew Lim Tan

This paper proposes an effective approach to model the emotional space of words to infer their Sense Sentiment Similarity (SSS). SSS reflects the distance between the words regarding their senses and underlying sentiments. We propose a probabilistic approach that is built on a hidden emotional model in which the basic human emotions are considered as hidden. This leads to predict a vector of emotions for each sense of the words, and then to infer the sense sentiment similarity. The effectiveness of the proposed approach is investigated in two Natural Language Processing tasks: Indirect yes/no Question Answer Pairs Inference and Sentiment Orientation Prediction.

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TIST Journal 2013 Journal Article

Random walks down the mention graphs for event coreference resolution

Bin Chen
Jian Su
Chew Lim Tan

Event coreference is an important task in event extraction and other natural language processing tasks. Despite its importance, it was merely discussed in previous studies. In this article, we present a global coreference resolution system dedicated to various sophisticated event coreference phenomena. First, seven resolvers are utilized to resolve different event and object coreference mention pairs with a new instance selection strategy and new linguistic features. Second, a global solution—a modified random walk partitioning—is employed for the chain formation. Being the first attempt to apply the random walk model for coreference resolution, the revised model utilizes a sampling method, termination criterion, and stopping probability to greatly improve the effectiveness of random walk model for event coreference resolution. Last but not least, the new model facilitates a convenient way to incorporate sophisticated linguistic constraints and preferences, the related object mention graph, as well as pronoun coreference information not used in previous studies for effective chain formation. In total, these techniques impose more than 20% F-score improvement over the baseline system.

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AAAI Conference 2012 Conference Paper

Sense Sentiment Similarity: An Analysis

Mitra Mohtarami
Hadi Amiri
Man Lan
Thanh Phu Tran
Chew Lim Tan

This paper describes an emotion-based approach to acquire sentiment similarity of word pairs with respect to their senses. Sentiment similarity indicates the similarity between two words from their underlying sentiments. Our approach is built on a model which maps from senses of words to vectors of twelve basic emotions. The emotional vectors are used to measure the sentiment similarity of word pairs. We show the utility of measuring sentiment similarity in two main natural language processing tasks, namely, indirect yes/no question answer pairs (IQAP) Inference and sentiment orientation (SO) prediction. Extensive experiments demonstrate that our approach can effectively capture the sentiment similarity of word pairs and utilize this information to address the above mentioned tasks.

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IJCAI Conference 2011 Conference Paper

Entity Linking with Effective Acronym Expansion, Instance Selection, and Topic Modeling

Wei Zhang
Yan Chuan Sim
Jian Su
Chew Lim Tan

Entity linking maps name mentions in the documents to entries in a knowledge base through resolving the name variations and ambiguities. In this paper, we propose three advancements for entity linking. Firstly, expanding acronyms can effectively reduce the ambiguity of the acronym mentions. However, only rule-based approaches relying heavily on the presence of text markers have been used for entity linking. In this paper, we propose a supervised learning algorithm to expand more complicated acronyms encountered, which leads to 15. 1% accuracy improvement over state-of-the-art acronym expansion methods. Secondly, as entity linking annotation is expensive and labor intensive, to automate the annotation process without compromise of accuracy, we propose an instance selection strategy to effectively utilize the automatically generated annotation. In our selection strategy, an informative and diverse set of instances are selected for effective disambiguation. Lastly, topic modeling is used to model the semantic topics of the articles. These advancements give statistical significant improvement to entity linking individually. Collectively they lead the highest performance on KBP-2010 task.

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AAAI Conference 2011 Conference Paper

Tree Sequence Kernel for Natural Language

Jun Sun
Min Zhang
Chew Lim Tan

We propose Tree Sequence Kernel (TSK), which implicitly exhausts the structure features of a sequence of subtrees embedded in the phrasal parse tree. By incorporating the capability of sequence kernel, TSK enriches tree kernel with tree sequence features so that it may provide additional useful patterns for machine learning applications. Two approaches of penalizing the substructures are proposed and both can be accomplished by efﬁcient algorithms via dynamic programming. Evaluations are performed on two natural language tasks, i. e. Question Classiﬁcation and Relation Extraction. Experimental results suggest that TSK outperforms tree kernel for both tasks, which also reveals that the structure features made up of multiple subtrees are effective and play a complementary role to the single tree structure.

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