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In this paper, we describe a system which predicts phrase-level tags for eojeols in Korean using entropy inspired discriminative probabilistic models such as a conditional random fields. Instead of selecting features by the intuition of user, we use a decision tree and error analysis systematically for selecting the best feature. Once we generate all available features from the corpus, then select...
In this paper, a new method of multiple layer classifiers integration based on single classifier is proposed which called Auto Weight Adjust. In the most used classifiers, Maximum Entropy (ME) model has excellent performance, and Naïve Bayesian (NB) is preferred by researchers for it's simple and useful. So in our experiments we chose ME and NB as single classifiers and use the ME classifier result...
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...
Parts of speech tagging forms the important pre-processing step in many of the natural language processing applications like text summarization, question answering and information retrieval system. MorphoSyntactic disambiguation (part of speech tagging) is the process of classifying every word in a given context to its appropriate part of speech. In this paper, we first review all the supervised machine...
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...
Reports generated by soldiers are common in time-critical military environments. Data fusion systems that attempt to process those reports must maintain the context for each set of observations to avoid inaccurate state estimates. This paper analyzes the selection and assignment of topical context under a Bayesian methodology. We present several techniques to decrease the hypothesis space and heuristics...
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