Highlights: Language- and domain-independent automatic indexing terms for abstracting

Cohen, Jonathan

doi:10.1002/(sici)1097-4571(199504)46:3<162::aid-asi2>3.0.co;2-6

Cited by 108 publications

(72 citation statements)

References 29 publications

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“…This approach uses n-grams to discover words that match and "nearly" match target terms, then add these additional terms to the original query. This approach has wide appeal since it could be largely language independent and could be applied to various concept (word) representations such as phonemes, soundex codes [14,13] or for spelling correction [12], using differing retrieval engines [2], or as a means to summarize the content of a document [3].…”

Section: N-grams Based Query Term Expansionmentioning

confidence: 99%

Probabilistic retrieval of OCR degraded text using N-grams

Harding

Croft

Weir

1997

Research and Advanced Technology for Digital Libraries

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Abstract. The retrieval of OCR degraded text using n-gram formulations within a probabilistic retrieval system is examined in this paper. Direct retrieval of documents using mgram databases of 2 and 3-grams or 2, 3, 4 and 5-grams resulted in improved retrieval performance over standard (word based) queries on the same data when a level of 10 percent degradation or worse was achieved. A second method of using n-grams to identify appropriate matching and near matching terms for query expansion which also performed better than using standard queries is also described. This method was less effective than direct n-gram query formulations but can likely be improved with alternative query component weighting schemes and measures of term similarity. Finally, a web based retrieval application using n-gram retrieval of OCR text and display, with query term highlighting, of the source document image is described.

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Section: N-grams Based Query Term Expansionmentioning

confidence: 99%

Probabilistic retrieval of OCR degraded text using N-grams

Harding

Croft

Weir

1997

Research and Advanced Technology for Digital Libraries

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“…Guangyi Li, Houfeng Wang [4] suggest an improved automatic keyword extraction based on textrank using domain knowledge. They focused keyword extraction for Chinese scientific articles, they used a framework for selecting candidate keywords by Document Frequency Accessor Variety (DF AV) and a TextRank algorithm to improve the performance of keyword extraction, they considered keywords for a specific domain.…”

Section: Parts Of Speech Filtering:-mentioning

confidence: 99%

Keyword Extraction: A Comparative Study Using Graph Based Model and Rake.

Subramanian¹,

Karthik²

2017

IJAR

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“…The keywords in the document can be identified using the statistical information of the words. Cohen [8] used N gram statistical information to index the document. Other statistical methods that are used for keyword extraction include word frequency, term frequency [9], word co-occurrences etc.…”

Section: Related Workmentioning

confidence: 99%

A Statistical Approach of Keyword Extraction for Efficient Retrieval

Luthra¹,

Arora²,

Mittal³

et al. 2017

IJCA

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Large number of techniques for keyword extraction have been proposed for better matching of documents with the user's query but most of them deal with tf-idf to find the weight age of query terms in the entire document but this can result in improper result as if a term has a low term frequency in overall document but high frequency in a certain part of the document then that term can be ignored by traditional tf-idf method. Through this paper, the keyword extraction is improved using a hybrid technique in which the entire document is split into multiple domains using a master keyword and the frequency of all unique words is found in every domain . The words having high frequency are selected as candidate keywords and the final selection is made on the basis of a graph which is constructed between the keywords using Word Net. The experiments, conducted on various documents show that proposed approach outperforms other keyword extraction methodologies by enhancing document retrieval.

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Highlights: Language- and domain-independent automatic indexing terms for abstracting

Cited by 108 publications

References 29 publications

Probabilistic retrieval of OCR degraded text using N-grams

Probabilistic retrieval of OCR degraded text using N-grams

Keyword Extraction: A Comparative Study Using Graph Based Model and Rake.

A Statistical Approach of Keyword Extraction for Efficient Retrieval

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