Latent Semantic Analysis Model as a Representation of Free-Association Word Norms

Ortega-Pacheco, David; Arias‐Trejo, Natalia; Martínez, Julia Blanco

doi:10.1109/micai.2012.13

Cited by 3 publications

(1 citation statement)

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“…As a result, each word in the corpus is related to all co-occurring words and to all texts in which it occurs. The LSA algorithm may be applied in various domains-from a text content comparison [8] to an analysis of human association norm [9]. Unfortunately, there is still little interest in studying the linguistic significance of LSA-made associations.…”

Section: Related Workmentioning

confidence: 99%

Extracting Semantic Prototypes and Factual Information from a Large Scale Corpus Using Variable Size Window Topic Modelling

Korzycki

Korczyński

2014

Annals of Computer Science and Information Systems

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Abstract-In this paper a model of textual events composed of a mixture of semantic stereotypes and factual information is proposed. A method is introduced that enables distinguishing automatically semantic prototypes of a general nature describing general categories of events from factual elements specific to a given event. Next, this paper presents the results of an experiment of unsupervised topic extraction performed on documents from a large-scale corpus with an additional temporal structure. This experiment was realized as a comparison of the nature of information provided by Latent Dirichlet Allocation and Vector Space modelling based on Log-Entropy weights. The impact of using different time windows of the corpus on the results of topic modelling is presented. Finally, a discussion is suggested on the issue if unsupervised topic modelling may reflect deeper semantic information, such as elements describing a given event or its causes and results, and discern it from pure factual data.

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Section: Related Workmentioning

confidence: 99%