Contextual Similarity: Quasilinear-Time Search and Comparison for Sequential Data

Karic, Ilhan; Vejzović, Zanin

doi:10.1109/iccairo.2017.15

Cited by 1 publication

(6 citation statements)

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“…When it comes to the effect of sequence similarity on sentence retrieval, we made use of the contextual similarity functions presented by the authors of [27]. This procedure enabled us to match of terms that were not identical but similar.…”

Section: Sentence Retrieval Using Sequence Similaritymentioning

confidence: 99%

“…This procedure enabled us to match of terms that were not identical but similar. Through analyzing the equation for the assessment of the common context by the authors of [27], we concluded that the same analogy could be used to find out if a certain term from query q and a certain term from sentence s share a common subsequence.…”

Section: Sentence Retrieval Using Sequence Similaritymentioning

confidence: 99%

“…In Reference [27], the formula δ(N, M) determines the appearance of subsequence N in a sequence M. We can define the formula δ(q, s) analogous to the formula from Reference [27], with query q (instead of subsequence in [27]) and sentence s (instead of sequence M in [27]) as follows:…”

Section: Sentence Retrieval Using Sequence Similaritymentioning

confidence: 99%

“…If sequence s does not contain the subsequence q ij , there is no need to check for qi( j + 1) [27].…”

Section: Sentence Retrieval Using Sequence Similaritymentioning

confidence: 99%

“…When the sequence q is a subsequence of s, the δ(q, s) = 1. Furthermore, we have to extend Formula (6) to include the normalization parameter T σ that is given in work [27]. This solves the measurement problem that appears when larger sequences that have more subsequences are more similar than the sequences with shorter lengths, which is not correct [27].…”

Section: Sentence Retrieval Using Sequence Similaritymentioning

confidence: 99%

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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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Section: Sentence Retrieval Using Sequence Similaritymentioning

confidence: 99%

Section: Sentence Retrieval Using Sequence Similaritymentioning

confidence: 99%