Using TF-ISF with Local Context to Generate an Owl Document Representation for Sentence Retrieval

Abstract

In this paper we combine our previous research in the field of Semantic web, especially ontology learning and population with Sentence retrieval. To do this we developed a new approach to sentence retrieval modifying our previous TF-ISF method which uses local context information to take into account only document level information. This is quite a new approach to sentence retrieval, presented for the first time in this paper and also compared to the existing methods that use information from whole document collection. Using this approach and developed methods for sentence retrieval on a document level it is possible to assess the relevance of a sentence by using only the information from the retrieved sentence's document and to define a document level OWL representation for sentence retrieval that can be automatically populated. In this way the idea of Semantic Web through automatic and semi-automatic extraction of additional information from existing web resources is supported. Additional information is formatted in OWL document containing document sentence relevance for sentence retrieval.

Sentence Retrieval using Stemming and Lemmatization with Different Length of the Queries

“…Sentence retrieval is similar to document retrieval, and document retrieval methods can be adapted for sentence retrieval [7]. When it comes to Document retrieval the State of The Art − (term frequencyinverse document frequency) method is commonly combined with preprocessing steps stemming and stop word removal.…”

Improving Sentence Retrieval Using Sequence Similarity

“…Sentence retrieval is similar to document retrieval and it's defined as the task of acquiring relevant sentences as a response to a query, question, or reference sentence [2] or, simply, task of finding relevant sentences from a document [3,4].…”

“…It can be used in various ways to simplify the end user task of finding the right information from document collections [4]. One of the first and most successful methods for sentence retrieval is the term frequency-inverse sentence frequency ( TF-ISF) method, which is an adaptation of the term frequency-inverse document frequency ( TF-IDF) method to sentence retrieval [3,5]. Also, BM25 and language modeling-based methods are used for sentence retrieval where the sentence is the unit of retrieval [6].…”