Improved sentence retrieval using local context and sentence length

Doko, Alen; Štula, Maja; Šerić, Ljiljana

doi:10.1016/j.ipm.2013.06.004

Cited by 2 publications

(9 citation statements)

References 14 publications

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“…In [18] it was shown that it is possible to improve the TF-ISF ranking function using local context and named the new method TF-ISF con . In [18] the method TF-ISFcon was derived from R(s|q) as follows:…”

Section: Document Level Tf-isf and Document Level Tf-isf Conmentioning

confidence: 99%

“…The TF-ISF based ranking function for sentence retrieval is [8,14]: In [18] the previous and next sentence in the same document were used as the local context of each sentence. It was assumed that relevance of a sentence depends partially on the information within the sentence itself and partially on information within the two closest neighboring sentences.…”

Section: Document Level Tf-isf and Document Level Tf-isf Conmentioning

confidence: 99%

“…ܴ ሺ‫ݍ|ݏ‬ሻ = ܴሺ‫ݍ|ݏ‬ሻ and the recurrence stops. In [18] this sentence retrieval method was called TF-ISF con .…”

Section: Document Level Tf-isf and Document Level Tf-isf Conmentioning

confidence: 99%

“…Using findings from previous work [18] and Section 2. we suggest that it is possible to develop a semantic web approach to sentence retrieval. In other words it is possible to define an OWL representation of a textual document that can be used for sentence retrieval.…”

Section: An Owl Document Representation For Sentence Retrievalmentioning

confidence: 99%

“…• In [18] we showed how the simple structure of a plain text document (sentence and neighboring sentences) can additionally be exploited to improve the vector space model of sentence retrieval.…”

Section: An Owl Document Representation For Sentence Retrievalmentioning

confidence: 99%

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

Doko¹,

Štula²,

Šerić³

2015

CSEIJ

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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.

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Section: Document Level Tf-isf and Document Level Tf-isf Conmentioning

confidence: 99%

Section: Document Level Tf-isf and Document Level Tf-isf Conmentioning

confidence: 99%

“…ܴ ሺ‫ݍ|ݏ‬ሻ = ܴሺ‫ݍ|ݏ‬ሻ and the recurrence stops. In [18] this sentence retrieval method was called TF-ISF con .…”

Section: Document Level Tf-isf and Document Level Tf-isf Conmentioning

confidence: 99%

Section: An Owl Document Representation For Sentence Retrievalmentioning

confidence: 99%

Section: An Owl Document Representation For Sentence Retrievalmentioning

confidence: 99%

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

Doko¹,

Štula²,

Šerić³

2015

CSEIJ

Self Cite

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Improving Sentence Retrieval Using Sequence Similarity

2020

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Sentence retrieval is an information retrieval technique that aims to find sentences corresponding to an information need. It is used for tasks like question answering (QA) or novelty detection. Since it is similar to document retrieval but with a smaller unit of retrieval, methods for document retrieval are also used for sentence retrieval like term frequency—inverse document frequency (TF-IDF), BM 25 , and language modeling-based methods. The effect of partial matching of words to sentence retrieval is an issue that has not been analyzed. We think that there is a substantial potential for the improvement of sentence retrieval methods if we consider this approach. We adapted TF-ISF, BM 25 , and language modeling-based methods to test the partial matching of terms through combining sentence retrieval with sequence similarity, which allows matching of words that are similar but not identical. All tests were conducted using data from the novelty tracks of the Text Retrieval Conference (TREC). The scope of this paper was to find out if such approach is generally beneficial to sentence retrieval. However, we did not examine in depth how partial matching helps or hinders the finding of relevant sentences.

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Improved sentence retrieval using local context and sentence length

Cited by 2 publications

References 14 publications

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

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

Improving Sentence Retrieval Using Sequence Similarity

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