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  • × author_ss:"Li, C."
  • × author_ss:"Sun, A."
  • × year_i:[2010 TO 2020}
  1. Li, C.; Sun, A.; Datta, A.: TSDW: Two-stage word sense disambiguation using Wikipedia (2013) 0.00 
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    Abstract
    The semantic knowledge of Wikipedia has proved to be useful for many tasks, for example, named entity disambiguation. Among these applications, the task of identifying the word sense based on Wikipedia is a crucial component because the output of this component is often used in subsequent tasks. In this article, we present a two-stage framework (called TSDW) for word sense disambiguation using knowledge latent in Wikipedia. The disambiguation of a given phrase is applied through a two-stage disambiguation process: (a) The first-stage disambiguation explores the contextual semantic information, where the noisy information is pruned for better effectiveness and efficiency; and (b) the second-stage disambiguation explores the disambiguated phrases of high confidence from the first stage to achieve better redisambiguation decisions for the phrases that are difficult to disambiguate in the first stage. Moreover, existing studies have addressed the disambiguation problem for English text only. Considering the popular usage of Wikipedia in different languages, we study the performance of TSDW and the existing state-of-the-art approaches over both English and Traditional Chinese articles. The experimental results show that TSDW generalizes well to different semantic relatedness measures and text in different languages. More important, TSDW significantly outperforms the state-of-the-art approaches with both better effectiveness and efficiency.
    Type
    a
  2. Li, C.; Sun, A.: Extracting fine-grained location with temporal awareness in tweets : a two-stage approach (2017) 0.00 
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    Abstract
    Twitter has attracted billions of users for life logging and sharing activities and opinions. In their tweets, users often reveal their location information and short-term visiting histories or plans. Capturing user's short-term activities could benefit many applications for providing the right context at the right time and location. In this paper we are interested in extracting locations mentioned in tweets at fine-grained granularity, with temporal awareness. Specifically, we recognize the points-of-interest (POIs) mentioned in a tweet and predict whether the user has visited, is currently at, or will soon visit the mentioned POIs. A POI can be a restaurant, a shopping mall, a bookstore, or any other fine-grained location. Our proposed framework, named TS-Petar (Two-Stage POI Extractor with Temporal Awareness), consists of two main components: a POI inventory and a two-stage time-aware POI tagger. The POI inventory is built by exploiting the crowd wisdom of the Foursquare community. It contains both POIs' formal names and their informal abbreviations, commonly observed in Foursquare check-ins. The time-aware POI tagger, based on the Conditional Random Field (CRF) model, is devised to disambiguate the POI mentions and to resolve their associated temporal awareness accordingly. Three sets of contextual features (linguistic, temporal, and inventory features) and two labeling schema features (OP and BILOU schemas) are explored for the time-aware POI extraction task. Our empirical study shows that the subtask of POI disambiguation and the subtask of temporal awareness resolution call for different feature settings for best performance. We have also evaluated the proposed TS-Petar against several strong baseline methods. The experimental results demonstrate that the two-stage approach achieves the best accuracy and outperforms all baseline methods in terms of both effectiveness and efficiency.
    Type
    a
  3. Qu, B.; Cong, G.; Li, C.; Sun, A.; Chen, H.: ¬An evaluation of classification models for question topic categorization (2012) 0.00 
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    Abstract
    We study the problem of question topic classification using a very large real-world Community Question Answering (CQA) dataset from Yahoo! Answers. The dataset comprises 3.9 million questions and these questions are organized into more than 1,000 categories in a hierarchy. To the best knowledge, this is the first systematic evaluation of the performance of different classification methods on question topic classification as well as short texts. Specifically, we empirically evaluate the following in classifying questions into CQA categories: (a) the usefulness of n-gram features and bag-of-word features; (b) the performance of three standard classification algorithms (naive Bayes, maximum entropy, and support vector machines); (c) the performance of the state-of-the-art hierarchical classification algorithms; (d) the effect of training data size on performance; and (e) the effectiveness of the different components of CQA data, including subject, content, asker, and the best answer. The experimental results show what aspects are important for question topic classification in terms of both effectiveness and efficiency. We believe that the experimental findings from this study will be useful in real-world classification problems.
    Type
    a

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