Evaluation of LSA performance in Spanish using multiple corpus of text
Evaluation of LSA performance in Spanish using multiple corpus of text
Latent Semantic Analysis is a natural language processing tools that allows estimating semantic distance between terms. The success of LSA is mainly based on the training corpus choice, which have been studied principally in English. This study focuses on studying LSA with regional Spanish corpus and evaluate the performance by identifying synonyms. We found that performance was slightly better than chance, concordantly with previous results. Standard LSA method cannot dynamically increase the training corpus. By using classifiers we combined multiple LSA models and showed that the use of automatic classifiers increase the performance.
Sociedad Argentina de Informática e Investigación Operativa
- National University of La Plata Argentina
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
regional Spanish corpus, Latent Semantic Analysis, Ciencias Informáticas, Natural Language Processing
regional Spanish corpus, Latent Semantic Analysis, Ciencias Informáticas, Natural Language Processing
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Popularity provided by BIP!- National University of La Plata Argentina
Latent Semantic Analysis is a natural language processing tools that allows estimating semantic distance between terms. The success of LSA is mainly based on the training corpus choice, which have been studied principally in English. This study focuses on studying LSA with regional Spanish corpus and evaluate the performance by identifying synonyms. We found that performance was slightly better than chance, concordantly with previous results. Standard LSA method cannot dynamically increase the training corpus. By using classifiers we combined multiple LSA models and showed that the use of automatic classifiers increase the performance.
Sociedad Argentina de Informática e Investigación Operativa