Kang Liu

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Kang Liu is an author.

Publications

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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Cross lingual entity linking with bilingual topic model IJCAI International Joint Conference on Artificial Intelligence English 2013 Cross lingual entity linking means linking an entity mention in a background source document in one language with the corresponding real world entity in a knowledge base written in the other language. The key problem is to measure the similarity score between the context of the entity mention and the document of the cand idate entity. This paper presents a general framework for doing cross lingual entity linking by leveraging a large scale and bilingual knowledge base, Wikipedia. We introduce a bilingual topic model that mining bilingual topic from this knowledge base with the assumption that the same Wikipedia concept documents of two different languages share the same semantic topic distribution. The extracted topics have two types of representation, with each type corresponding to one language. Thus both the context of the entity mention and the document of the cand idate entity can be represented in a space using the same semantic topics. We use these topics to do cross lingual entity linking. Experimental results show that the proposed approach can obtain the competitive results compared with the state-of-art approach. 0 0
Determining relation semantics by mapping relation phrases to knowledge base Open Information Extraction
Relation Mapping
Wikipedia Infobox
Proceedings - 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013 English 2013 0 0
Are human-input seeds good enough for entity set expansion? Seeds rewriting by leveraging Wikipedia semantic knowledge Information extraction
Seed rewrite
Semantic knowledge
Lecture Notes in Computer Science English 2012 Entity Set Expansion is an important task for open information extraction, which refers to expanding a given partial seed set to a more complete set that belongs to the same semantic class. Many previous researches have proved that the quality of seeds can influence expansion performance a lot since human-input seeds may be ambiguous, sparse etc. In this paper, we propose a novel method which can generate new, high-quality seeds and replace original, poor-quality ones. In our method, we leverage Wikipedia as a semantic knowledge to measure semantic relatedness and ambiguity of each seed. Moreover, to avoid the sparseness of the seed, we use web resources to measure its population. Then new seeds are generated to replace original, poor-quality seeds. Experimental results show that new seed sets generated by our method can improve entity expansion performance by up to average 9.1% over original seed sets. 0 0
Choosing better seeds for entity set expansion by leveraging wikipedia semantic knowledge Information extraction
Seed set refinement
Semantic knowledge
Communications in Computer and Information Science English 2012 Entity Set Expansion, which refers to expanding a human-input seed set to a more complete set which belongs to the same semantic category, is an important task for open information extraction. Because human-input seeds may be ambiguous, sparse etc., the quality of seeds has a great influence on expansion performance, which has been proved by many previous researches. To improve seeds quality, this paper proposes a novel method which can choose better seeds from original input ones. In our method, we leverage Wikipedia semantic knowledge to measure semantic relatedness and ambiguity of each seed. Moreover, to avoid the sparseness of the seed, we use web corpus to measure its population. Lastly, we use a linear model to combine these factors to determine the final selection. Experimental results show that new seed sets chosen by our method can improve expansion performance by up to average 13.4% over random selected seed sets. 0 0
Exploring the existing category hierarchy to automatically label the newly-arising topics in cQA Category hierarchy
Community question answering
Newly-arising topics
ACM International Conference Proceeding Series English 2012 This work investigates selecting concise labels for the newly-arising topics in community question answer. Previous methods of generating labels do not take the information of the existing category hierarchy into consideration. The main motivation of our paper is to utilize this information into the label generation process. We propose a general framework to address this problem. Firstly, we map the questions into Wikipedia concept sets, which are more meaningful than terms. Secondly, important concepts are identified to represent the main focus of the newly-arising topics. Thirdly, candidate labels are extracted from Wikipedia category graph. Finally, candidate labels are filtered and reranked by combination of structure information of existing category hierarchy and Wikipedia category graph. The experiments show that in our test collections, about 80% "correct" labels appear in the top ten labels recommended by our system. 0 0
Large-scale question classification in cQA by leveraging Wikipedia semantic knowledge Large-scale classification
Question retrieval
Translation model
Wikipedia
CIKM English 2011 0 0