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Clustering is a distribution of data into groups of similar objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups. The concept of clustering applications is particularly in the context of information retrieval and in organizing web resources. The objective of clustering is to find out information and in...
Various Markov models have been proposed to model individuals' mobility, i.e., the transitions between locations. Although these studies are able to show high predicting accuracy of individuals' next move, two basic assumptions of these studies, namely the stationarity of individuals' mobility sequence and the dependency of visiting the locations, have never been validated. Moreover, a famous recent...
One nodus existing in Chinese word segmentation is the ambiguity problem of which more than 85% are crossing ambiguity, therefore it is significant to decrease the error in dealing with the crossing ambiguity. Taking the advantage of the characteristics of the crossing ambiguity string, a novel method based on the mutual information and t-test difference is proposed to deal with the ambiguities in...
There are many connotative semantic features in Chinese which can help Chinese named entity recognition. Moreover, one of the important strongpoint of maximum entropy model is that it can syncretize features in different granularity and level. With that in mind, many Chinese named entity semantic knowledge bases were established by extracting information from corpus in this paper. However, because...
This paper presents a maximum entropy tagger for the identification of intra-sentential temporal relations between temporal expressions and eventualities mediated by temporal signals in constructions of the kind "eventuality + signal + temporal relation". The tagger reports an accuracy rate of 90.8%, outperforming the baseline (81.8%). One of the main results of this work is represented...
Natural languages are typically replete with homographs, words which have more than one meaning. Consequently, machine understanding of natural language sentences sometimes suffers from certain ambiguities in getting the correct sense of a word in a given sentence. In this work we present a trainable model for word sense disambiguation (WSD) for resolving this ambiguity. The proposed model applies...
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