Effect of tuned parameters on an LSA multiple choice questions answering model

Lifchitz, Alain; Jhean-Larose, Sandra; Denhière, Guy

doi:10.3758/brm.41.4.1201

Cited by 18 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recently, Lifchitz et al (2009) explored the parameter space of LSA (Landauer & Dumais, 1997) to find the optimal dimensionality of their model across various corpora of different sizes. They found that their optimal tuning of lemmatization, stop-word lists, term weighting, pseudodocuments, and normalization of document vectors in LSA allowed their model to outperform seventh-and eighth-grade students on a multiple-choice biology test.…”

Section: Exploring Parameter Space With Hidexmentioning

confidence: 99%

“…They found that their optimal tuning of lemmatization, stop-word lists, term weighting, pseudodocuments, and normalization of document vectors in LSA allowed their model to outperform seventh-and eighth-grade students on a multiple-choice biology test. Both of these studies (Bullinaria & Levy, 2007;Lifchitz et al, 2009) show how exploring the parameter space of a high-dimensional model can lead to new insights and unexpected optimal-parameter sets.…”

Section: Exploring Parameter Space With Hidexmentioning

confidence: 99%

See 1 more Smart Citation

Exploring lexical co-occurrence space using HiDEx

Shaoul

Westbury

2010

Behavior Research Methods

120

141

View full text Add to dashboard Cite

This work investigates a class of models of lexical semantics derived from the hyperspace analog to language (HAL; Burgess, 1998;Burgess & Lund, 2000), a computational model of word meaning that derives semantic relationships from lexical co-occurrence. Although the original HAL model was well specified, it contains several parameters whose values were set without formal or empirical justification. We have created a novel and freely available implementation of the HAL model-called High Dimensional Explorer (HiDEx)-that allows users to systematically vary those parameters, creating a class of models that are algorithmically identical but parameterized differently. Given the absence of any a priori formal justifications for parameter values in HAL, we have elected to assess different parameter settings by how well they perform at predicting human behavioral data on two lexical access tasks: lexical decision and semantic decision. In this article we explain how HiDEx works and how we were able to use it to explore HAL's parameter space.We begin with a brief overview of the HAL class of models and some related models. HAL uses word cooccurrence to build a vector space that contains contextual information for every word in a specified dictionary. A vector space is a geometric representation of data that has an ordered set of N numbers associated with each point in an N-dimensional space. Each such set of numbers defines the point's location in the space and is called its vector. HAL space is made up of vectors with one dimension for each word in the language. In the original HAL work, these word vectors had more than 100,000 dimensions.Each point in a word's vector is a weighted count of the number of times another word co-occurs with that word in a corpus of text. Words can co-occur when they are adjacent or when they are separated by a small number of intervening words. The maximum distance between words considered to co-occur is called the window size. Window size is one of the free parameters in the HAL model. In the original model, words were considered to have cooccurred if they occurred within 10 words of each other in either direction.Words in another word's co-occurrence window are weighted according to their proximity to that word, using a weighting function. The original HAL model used a linear weighting function called a linear ramp as a multiplier to give more weight to the words that co-occurred closer to the center of the window. Words that occurred on either side of the center word of the window were assigned 10 co-occurrence points. Each word's outside neighbors were assigned 9 co-occurrence points, and so on, down to a single point for a word that occurred 10 words away from the center word. This weighting function is another free parameter in HAL that has no a priori justification and can be changed in HiDEx.Lexical memories in the HAL model are built by making the model read words in text one window at a time and then sliding the window forward one word. This process of counting local co-occurrenc...

show abstract

Section: Exploring Parameter Space With Hidexmentioning

confidence: 99%

Section: Exploring Parameter Space With Hidexmentioning

confidence: 99%

Exploring lexical co-occurrence space using HiDEx

Shaoul

Westbury

2010

Behavior Research Methods

120

141

View full text Add to dashboard Cite

show abstract

“…A single set of parameter values were used for each dataset reported in this paper, and these values were estimated arbitrarily. The optimization issues of LSA have been studied in Lifchitz, Jhean-Larose, and Denhière (2009) regarding optimal tuning of lemmatization, stop-word list, term weighting, pseudodocuments, and normalization of document vectors (see also Shaoul & Westbury, 2010). It is also possible to combine multiple models, for example, by regression analysis or AdaBoost (Freund & Schapire, 1997; see also Bishop, 2006), to improve the model performance.…”

Section: Discussionmentioning

confidence: 99%

Predicting raters’ transparency judgments of English and Chinese morphological constituents using latent semantic analysis

et al. 2013

View full text Add to dashboard Cite

The morphological constituents of English compounds (e.g., “butter” and “fly” for “butterfly”) and two-character Chinese compounds may differ in meaning from the whole word. Subjective differences and ambiguity of transparency make the judgments difficult, and a computational alternative based on a general model may be a way to average across subjective differences. The current study proposes two approaches based on Latent Semantic Analysis (Landauer & Dumais, 1997): Model 1 compares the semantic similarity between a compound word and each of its constituents, and Model 2 derives the dominant meaning of a constituent based on a clustering analysis of morphological family members (e.g., “butterfingers” or “buttermilk” for “butter”). The proposed models successfully predicted participants’ transparency ratings, and we recommend that experimenters use Model 1 for English compounds and Model 2 for Chinese compounds, due to raters’ morphological processing in different writing systems. The dominance of lexical meaning, semantic transparency, and the average similarity between all pairs within a morphological family are provided, and practical applications for future studies are discussed.

show abstract

“…Probabilistic latent semantic analysis is also used to find similarities between small groups of terms. In particular, in [7] the multiple choice questions (MCQ) answering model using probabilistic latent semantic analysis was described. In [8] the use of probabilistic latent semantic analysis for machine learning tasks and text data mining was specified.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

The use of probabilistic latent semantic analysis to identify scientific subject spaces and to evaluate the completeness of covering the results of dissertation studies

Lizunov¹,

Biloshchytskyi²,

Kuchansky³

et al. 2020

EEJET

View full text Add to dashboard Cite

The study considers the possibilities of using latent semantic analysis for the tasks of identifying scientific subject spaces and evaluating the completeness of covering the results of dissertation research by science degree seekers. A probabilistic thematic model was built to make it possible to cluster the publications of scholars in scientific areas, taking into account the citation network, which was an important step for solving the problem of identifying scientific subject spaces. As a result of constructing the model, the problem of increasing instability of clustering the citation graph in connection with a decrease in the number of clusters was solved. This problem would arise when combining clusters built on the basis of citation graph clustering, taking into account the similarity of abstracts of scientific publications. In the article, the presentation of text documents is described based on a probabilistic thematic model using n-grams. A probabilistic thematic model was built for the task of determining the completeness of covering the materials of an author's dissertation research in scientific publications. The approximate values of the threshold coefficients were calculated to evaluate whether the articles of an author included the research provisions that were reflected in the text of the author's abstract of the dissertation. The probabilistic thematic model for an author's publications was practised on the basis of the BigARTM tool. Using the constructed model and with the help of a special regularizer, a matrix was found to evaluate the relevance of topics specified by the segments of an author's dissertation abstracts to documents that are produced by the author's publications. Important aspects of the possibilities of using latent semantic analysis were studied to identify tasks of scientific subject spaces and to reveal the completeness of covering the results of dissertation research science degree seekers

show abstract

Effect of tuned parameters on an LSA multiple choice questions answering model

Cited by 18 publications

References 14 publications

Exploring lexical co-occurrence space using HiDEx

Exploring lexical co-occurrence space using HiDEx

Predicting raters’ transparency judgments of English and Chinese morphological constituents using latent semantic analysis

The use of probabilistic latent semantic analysis to identify scientific subject spaces and to evaluate the completeness of covering the results of dissertation studies

Contact Info

Product

Resources

About