DISCOVERY OF FRAMES AND RELATIONSHIP FROM NATURAL LANGUAGE TEXT

Chakrabarti P.¹*, Basu J.K.²*
¹Sir Padampat Singhania University, Udaipur-313601, Rajasthan, India
²Heritage Institute of Technology, Kokata-700107, West Bengal, India
* Corresponding Author : basu.jayanta@yahoo.co.in

Received : -     Accepted : -     Published : 21-12-2010
Volume : 1     Issue : 2       Pages : 1 - 5
Int J Comput Intell Tech 1.2 (2010):1-5

Keywords : information discovery, extraction patterns, tuples, queries, frame discovery
Conflict of Interest : None declared

Purpose of this paper is to addresses the problem of information discovery in large collections of text. For users, one of the key problems in working with such collections is determining where to focus their attention. Text documents often contain valuable structured data that is hidden in regular English sentences. This data is best exploited if available as a relational table that we could use for answering precise queries or for running data mining tasks. We explore a technique for extracting such tables from document collections that requires only a handful of training examples from users. These examples are used to generate extraction patterns that in turn result in new tuples being extracted from the document collection. In selecting documents for examination, users must be able to formulate reasonably precise queries. Queries that are too broad will greatly reduce the efficiency of information discovery efforts by overwhelming the users with peripheral information. In order to formulate efficient queries, a mechanism is needed to automatically alert users regarding potentially interesting information contained within the collection. The idea can better be explained into two sub topics. One is extraction of relations from natural plain text and another is frame discovery and formation of word-net from language text. In this paper we have tried to explain how to extract the different kind of relationship between the words with the help of a frame net analysis diagram of an annotation layer software.

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