Text summarization solves the problem of presenting the information needed by a user in a compact form. There are different approaches to create well formed summaries in literature. One of the newest methods in text summarization is the Latent Semantic Analysis (LSA) method. In this thesis, different LSA based summarization algorithms are explained and two new LSA based summarization algorithms are proposed. The algorithms are evaluated on Turkish and English documents, and their performances are compared using their ROUGE scores.
Keywords can be considered as condensed versions of documents and short forms of their summaries. In this paper, the problem of automatic extraction of keywords from documents is treated as a supervised learning task. A lexical chain holds a set of semantically related words of a text and it can be said that a lexical chain represents the semantic content of a portion of the text. Although lexical chains have been extensively used in text summarization, their usage for keyword extraction problem has not been fully investigated. In this paper, a keyword extraction technique that uses lexical chains is described, and encouraging results are obtained.
Abstract.A m e c hanism for learning lexical correspondences between two languages from sets of translated sentence pairs is presented. These lexical level correspondences are learned using analogical reasoning between two translation examples. Given two translation examples, the similar parts of the sentences in the source language must correspond to the similar parts of the sentences in the target language. Similarly, the di erent parts must correspond to the respective parts in the translated sentences. The correspondences between similarities and between di erences are learned in the form of translation templates. A translation template is a generalized translation exemplar pair where some components are generalized by replacing them with variables in both sentences and establishing bindings between these variables. The learned translation templates are obtained by replacing di erences or similarities by v ariables. This approach has been implemented and tested on a set of sample training datasets and produced promising results for further investigation.
This paper provides a pragmatic analysis of some human-computer conversations carried out during the past six years within the context of the Loebner Prize Contest, an annual competition in which computers participate in Turing Tests. The Turing Test posits that to be granted intelligence, a computer should imitate human conversational behavior so well as to be indistinguishable from a real human being. We carried out an empirical study exploring the relationship between computers' violations of Grice's cooperative principle and conversational maxims, and their success in imitating human language use. Based on conversation analysis and a large survey, we found that different maxims have different effects when violated, but more often than not, when computers violate the maxims, they reveal their identity. The results indicate that Grice's cooperative principle is at work during conversations with computers. On the other hand, studying human-computer communication may require some modifications of existing frameworks in pragmatics because of certain characteristics of these conversational environments. Pragmatics constitutes a serious challenge to computational linguistics. While existing programs have other significant shortcomings, it may be that the biggest hurdle in developing computer programs which can successfully carry out conversations will be modeling the ability to 'cooperate'. 0 2002 Elsevier Science B.V. All rights reserved.
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