Titlebook: Computational Linguistics and Intelligent Text Processing; 13th International C Alexander Gelbukh Conference proceedings 2012 Springer-Verl

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Kaleidoscope

A Fast Subspace Text Categorization Method Using Parallel Classifiers speed up document search and reduce classifier training times. The data available to us is frequently divided into several broad domains with many sub-category levels. Each of these domains of data constitutes a subspace which can be processed separately. In this paper, separate classifiers of the

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Research on Text Categorization Based on a Weakly-Supervised Transfer Learning Methoding classification tasks in new area. Instead, we can take use of the already tagged documents in other domains to accomplish the automatic categorization task. By extracting linguistic information such as part-of-speech, semantic, co-occurrence of keywords, we construct a domain-adaptive transfer k

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Clustering Short Text and Its Evaluationication, a variety of short text could be defined mainly in terms of their length (e.g. sentence, paragraphs) and type (e.g. scientific papers, newspapers). Finding a clustering method that is able to cluster short text in general is difficult. In this paper, we cluster 4 different corpora with diff

bile648

eczema

Combining Flat and Structured Approaches for Temporal Slot Filling or: How Much to Compress?n and temporal aggregation. As in many other NLP tasks, a key challenge lies in capturing relations between text elements separated by a long context. We have observed that features derived from a structured text representation can help compressing the context and reducing ambiguity. On the other ha

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草率女

Automatically Generated Noun Lexicons for Event Extraction, they can be interpreted in an eventive or non-eventive reading). Therefore, weights representing the relative “eventiveness” of a noun can help for disambiguating event detection in texts..We applied our method on both French and English corpora. Our method has been applied to both French and Engl

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