Soft computing, Volume 21, Issue 3, pp 627–639, doi:ĭirectly from Springer, (2) Helena Gómez-Adorno, Ilia Markov, Grigori Ildar Batyrshin, David Pinto, Liliana Chanona-Hernández.ĭistributed document representation in the authorship attribution task for smallĬorpora. (1) Juan-Pablo Posadas-Durán, Helena Gómez-Adorno, Grigori Sidorov, Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Softĭocument representation in authorship attribution and author profiling. Annual Conference of the North American Fuzzy Information Helena Gómez-Adorno, Ilia Markov, David Pinto, Nahun Loya.Ĭomputing Text Similarity using Tree Editĭistance. Soft similarity of syntactic n-grams (Grigori Sidorov, Python script below and consult the publications. Separate them with comas apply recursively. Metalanguage is needed: take words (nodes) in bifurcations into brackets and Or continuous, when no bifurcations are considered. N-grams can be non-continuous, when bifurcations in paths are permitted, Syntax in machine learning (see publications below). N-grams = n-grams constructed by following paths in syntactic trees = using Soft Similarity and Soft Cosine Measure: Similarity ofįeatures in Vector Space Model. Of the original features and then consider the new feature space (Grigori Sidorov, Alexander Gelbukh, Helena We simply add new features that are similarity-weighted pairs Well-known WordNet similarity can be used as well.The same idea can be applied to similarity in VSM whileĪpplying machine learning algorithms: the similarity is tranformed into '' soft We use Levenshtein distance forĬalculation of the similarity between features, measured in characters or inĮlements of n-grams. Similarity has 1s only at the diagonal, then these equations obtain the same That when the features are similar only to themselves, i.e., the matrix of
Introduce two equations that correspond to '' soft cosine measure''. Generalize the well-known cosine similarity measure in Vector Space Model: we It means that we add into the VSM each pair ofįeatures as the new feature weighted with their similarity. Similarity of pairs of features for calculation of similarity of objects in (1) Soft similarity and soft cosine measure.
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Parallel texts, linguistic software development, deep learning.Īutomatic analysis of explanatory dictionaries, sentiment and emotion analysis, authorshipĪttribution, syntactic n-grams, applications of word emeddings, applications of Text processing techniques and systems, automatic dictionary processing,Īutomatic morphological analysis of different languages, automatic syntacticĪnalysis, anaphora resolution, word sense disambiguation, corpus linguistics, We haveĬompetitive government scholarship (also for foreign students) sufficient for Web of Science (Scielo, CORE collection (emerging sources)), Scopus, DBLP, index of excellence of Conacyt, etc.) Phone: +(52)-55-57296000 ext.įor PhD and Master programs in our center are welcome. National Researcher of Mexico (SNI) level 3 (highest),
Mexico City, Mexico Regular member of Mexican Academy Instituto Politécnico Nacional (National Polytechnic Institute, Natural Language and Text Processing Laboratory,Ĭentro de Investigación en Computación (Center for Computing Research, Grigori SIDOROV PhD, Professor and researcher
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