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

Boban, Ivan; Doko, Alen; Gotovac, S.

doi:10.25046/aj050345

Cited by 24 publications

(14 citation statements)

References 15 publications

(17 reference statements)

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“…Our experiment was entirely focused on sentence retrieval, which represents the first task of novelty detection. Three data collections were used, each consisting of 50 topics (queries) and 25 documents per topic, with multiple sentences (Table 2) [30]. When it comes to TREC topics, it must be emphasized that each one has a title, a description, and narrative.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The P@10 values shown in this paper refer to average P@10 for 50 queries [30]. R-precision can be defined as [1]: Each TREC data collection also contains a list of relevant sentences.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…number of relevant sentences within top retrieved (12) The P@10 values shown in this paper refer to average P@10 for 50 queries [30]. R-precision can be defined as [1]:…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…High recall means it is more important to find all of the relevant sentences, even if it means searching through many sentences including many that are nonrelevant. Meanwhile, P@10 is used for testing precision [30].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In terms of information retrieval, precision means it is more important to get only relevant sentences than finding all of the relevant sentence [30].…”

Section: Experiments and Resultsmentioning

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: Experiments and Resultsmentioning

confidence: 99%

“…The P@10 values shown in this paper refer to average P@10 for 50 queries [30]. R-precision can be defined as [1]: Each TREC data collection also contains a list of relevant sentences.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…number of relevant sentences within top retrieved (12) The P@10 values shown in this paper refer to average P@10 for 50 queries [30]. R-precision can be defined as [1]:…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

“…In terms of information retrieval, precision means it is more important to get only relevant sentences than finding all of the relevant sentence [30].…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Improving Sentence Retrieval Using Sequence Similarity

2020

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A framework for investigating search engines' stemming mechanisms: A case study on Bing

Şentürk

Gunduz

2021

Concurrency and Computation

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Big data attracts the attention of governments and a lot of companies today. The developments in technology and the Internet make it one of the important sources of big data. It is easy to get lost in the enormous amount of information contained on the Internet if there were no search engines. Knowing how the search engines work will be helpful to access the desired information. This work aims to be a guide for accessing the right information and also to help to understand search engine stemming and indexing algorithm for interested parties. In this article, we have developed a framework that could be used to investigate the stemming mechanisms of search engines. Our framework also uses Word2vec to analyze semantic relations. We have used our framework to investigate the stemming algorithm of the search engine Bing for English language.In order to achieve that we have used this framework to select words, create queries, send them to Bing, and finally analyze the millions of returned results. We have discussed the results in the context of our article. The results indicate that our framework is useful for analyzing the stemming mechanisms of search engines.

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Geospatial Forecasting and Social Media Exploration Based on Sentiment Analysis: Application to Flood Forecasting

Abas

Addou

2021

Geospatial Intelligence

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

Cited by 24 publications

References 15 publications

Improving Sentence Retrieval Using Sequence Similarity

Improving Sentence Retrieval Using Sequence Similarity

A framework for investigating search engines' stemming mechanisms: A case study on Bing

Geospatial Forecasting and Social Media Exploration Based on Sentiment Analysis: Application to Flood Forecasting

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