Entity linking is the task of map entity mentions in a document to their entities in a knowledge base (KB). In this paper, we briefly introduce the traditional entity linking system and point out the key problem of entity linking system-the semantic similarity measure between the content of entity mention and the document of the candidate entity. And then, we propose a novel semantic relatedness measure between Wikipedia concepts based on the graph structure of Wikipedia. With this similarity measure, we present a novel learning to rank framework which leverage the rich semantic information derived from Wikipedia to deal with the entity lining task. Experiment results show that the performance of the system is comparable to the state-of-art result.

Some frequent sentiment words have strong semantic fuzziness, i.e., have ambiguous sentiment polarities. These words are particularly problematic in word-based sentiment analysis. In this paper, we design an approach to deal with this problem by combining rough set theory and Bayesian classification. To determine the sentiment polarity of a fuzzy word, we use a set of features extracted from its context of utilization. Decision rules based on the features are derived using rough sets. In case the rules fail to classify a case, a Bayes classifier is used as complement. We investigate the case of “HAO” in Chinese—a very frequent sentiment word, but with many different meanings. The experimental results on several datasets show that our combined method can effectively cope with the semantic fuzziness of the word and improve the quality of sentiment analysis.

PLSA((Probabilistic Latent Semantic Analysis) is a typical topic model. To enable a distributed computation of PLSA for the ever-increasing large datasets, a parallel PLSA algorithm based on MapReduce is proposed in this paper. Applied in text clustering and semantic analysis, the algorithm is demonstrated by the experiments for s its scalability in dealing with large datasets.