Yajie Miao

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Yajie Miao is an author.


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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Chinese named entity recognition and disambiguation based on wikipedia Named Entity Disambiguation
Named entity recognition
Communications in Computer and Information Science English 2012 This paper presents a method for named entity recognition and disambiguation based on Wikipedia. First, we establish Wikipedia database using open source tools named JWPL. Second, we extract the definition term from the first sentence of Wikipedia page and use it as external knowledge in named entity recognition. Finally, we achieve named entity disambiguation using Wikipedia disambiguation pages and contextual information. The experiments show that the use of Wikipedia features can improve the accuracy of named entity recognition. 0 0
Infinite topic modelling for trend tracking hierarchical dirichlet process approaches with wikipedia semantic based method Hierarchical dirichlet process
Temporal analysis
Topic modelling
KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval English 2012 The current affairs people concern closely vary in different periods and the evolution of trends corresponds to the reports of medias. This paper considers tracking trends by incorporating non-parametric Bayesian approaches with temporal information and presents two topic modelling methods. One utilizes an infinite temporal topic model which obtains the topic distribution over time by placing a time prior when discovering topics dynamically. In order to better organize the event trend, we present another progressive superposed topic model which simulates the whole evolutionary processes of topics, including new topics' generation, stable topics' evolution and old topics' vanishment, via a series of superposed topics distribution generated by hierarchical Dirichlet process. Both of the two approaches aim at solving the real-world task while avoiding Markov assumption and breaking the number limitation of topics. Meanwhile, we employ Wikipedia based semantic background knowledge to improve the discovered topics and their readability. The experiments are carried out on the corpus of BBC news about American Forum. The results demonstrate better organized topics, evolutionary processes of topics over time and model effectiveness. Copyright 0 0
Improving Question Answering Based on Query Expansion with Wikipedia Query Expansion
Question answering
ICTAI English 2010 0 0
Mining Wikipedia and Yahoo! Answers for question expansion in Opinion QA Opinion QA
Question expansion
Yahoo! answers
Lecture Notes in Computer Science English 2010 Opinion Question Answering (Opinion QA) is still a relatively new area in QA research. The achieved methods focus on combining sentiment analysis with the traditional Question Answering methods. Few attempts have been made to expand opinion questions with external background information. In this paper, we introduce the broad-mining and deep-mining strategies. Based on these two strategies, we propose four methods to exploit Wikipedia and Yahoo! Answers for enriching representation of questions in Opinion QA. The experimental results show that the proposed expansion methods perform effectively for improving existing Opinion QA models. 0 0
Mining wikipedia and yahoo! answers for question expansion in opinion QA Yahoo! answers
Opinion QA
Question expansion
PAKDD English 2010 0 0