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Updated: Aug 19, 2026

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
An implementation of the trigram phrase matching method for text similarity problems
Adalberto O Tardelli1, Meide S Anção, Abel L Packer
1Latin American and Caribbean Center for Health Science Information (BIREME/PAHO/WHO), São Paulo, Brazil.
Abstract:
The representation of texts by term vectors with element values calculated by a TFIDF method yields to significant results in text similarity problems, such as finding related documents in bibliographic or full-text databases and identifying MeSH concepts from medical texts by lexical approach and also harmonizing journal citation in ISI/SciELO references and normalizing author's affiliation in MEDLINE. Our work considered "trigrams" as the terms (elements) of a term vector representing a text, according to the Trigram Phrase Matching published by the NLM's Indexing Initiative and its logarithmic Term Frequency-Inverse Document Frequency method for term weighting. Trigrams are overlapping 3-char strings from a text, extracted by a couple of rules, and a trigram matching method may improve the probability of identifying synonym phrases or similar texts. The matching process was implemented as a simple algorithm, and requires a certain amount of computer resources. An efficiency-focused C-programming was adopted. In addition, some heuristic rules improved the efficiency of the method and made it feasible a regular "find your scientific production in SciELO collection" information service. We describe an implementation of the Trigram Matching method, the software tool we developed and a set of experimental parameters for the above results.
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