Latent semantic analysis

Landauer, Tom; Dooley, Scott

doi:10.3115/1658616.1658815

Cited by 17 publications

(22 citation statements)

References 6 publications

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“…However, the co-occurrence neighborhoods of words in nondecomposed models often include morphological variants of the same word (e.g., the first two neighbors of the word work are working and works), reflecting the fact that many morphological variants of the same root word share similar contexts. However, this is of course not always true; recall that (as cited above) Landauer and Dumais (2008) noted that Bstemming often confabulates meanings^(p. 4356) in word vectors.…”

Section: Performance Impact Of Morphological Decompositionmentioning

confidence: 97%

“…As suggested by Landauer and Dumais (2008), this compression of information may introduce excessive noise into the context, thereby reducing semantic content. Implicit in such a view, however, is the assumption that compressing-or Bconfabulating^-co-occurrence statistics into a combined representation would necessarily result in a loss of information.…”

Section: Current Studymentioning

confidence: 99%

“…Using the same word-stemming approach taken by Bullinaria and Levy (2012), Landauer, McNamara, Dennis, and Kintsch (2007)-using a standard LSA implementation-found that stemming offered no improvement; indeed, Landauer and Dumais (2008) claim that Bstemming often confabulates meanings^(p. 4356) in word vectors.…”

Section: Limitations Of Corpus-based Semantic Space Modelsmentioning

confidence: 99%

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Performance impact of stop lists and morphological decomposition on word–word corpus-based semantic space models

2015

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Corpus-based semantic space models, which primarily rely on lexical co-occurrence statistics, have proven effective in modeling and predicting human behavior in a number of experimental paradigms that explore semantic memory representation. The most widely studied extant models, however, are strongly influenced by orthographic word frequency (e.g., Shaoul & Westbury, Behavior Research Methods, 38, 190-195, 2006). This has the implication that high-frequency closed-class words can potentially bias co-occurrence statistics. Because these closed-class words are purported to carry primarily syntactic, rather than semantic, information, the performance of corpus-based semantic space models may be improved by excluding closedclass words (using stop lists) from co-occurrence statistics, while retaining their syntactic information through other means (e.g., part-of-speech tagging and/or affixes from inflected word forms). Additionally, very little work has been done to explore the effect of employing morphological decomposition on the inflected forms of words in corpora prior to compiling cooccurrence statistics, despite (controversial) evidence that humans perform early morphological decomposition in semantic processing. In this study, we explored the impact of these factors on corpus-based semantic space models. From this study, morphological decomposition appears to significantly improve performance in word-word co-occurrence semantic space models, providing some support for the claim that sublexical information-specifically, word morphologyplays a role in lexical semantic processing. An overall decrease in performance was observed in models employing stop lists (e.g., excluding closed-class words). Furthermore, we found some evidence that weakens the claim that closed-class words supply primarily syntactic information in word-word cooccurrence semantic space models. Human language, and the semantic representation it facilitates, is a complex behavior. To understand language, one needs to know the meaning of words, and retain knowledge regarding the grammatical application of words. The former requirement is addressed by lexical semantics, or the study of individual word meanings as constrained by morphology. Here, meaning is defined by context that is likely derived from statistical redundancies in multisensory elements perceived in environment-that is, more than those found in analyzing text alone. Using text alone is not likely to ever provide a comprehensive basis for modeling language comprehension, yet, it has been shown that many aspects of perception and cognition can be understood in isolation by modeling specific capacities as computational problems (Anderson, 1990;Marr, 1982). One such approach in acquiring an understanding of semantic representation involves using simple mechanism(s) operating on large scale. This approach has yielded a rich history of both high level and derived mechanistic memory models for lexical semantic representations. Many of these mechanistic models

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Section: Performance Impact Of Morphological Decompositionmentioning

confidence: 97%

Section: Current Studymentioning

confidence: 99%

Section: Limitations Of Corpus-based Semantic Space Modelsmentioning

confidence: 99%

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Performance impact of stop lists and morphological decomposition on word–word corpus-based semantic space models

2015

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“…In this section, we discuss our approaches of using latent semantic analysis (LSA) [10] and its related packages for terms and documents comparison to recover the most related discussion themes and potential target audiences to benefit affect detection.…”

Section: Semantic Interpretation Of Social Contextsmentioning

confidence: 99%

Affect Detection from Text-Based Virtual Improvisation and Emotional Gesture Recognition

Zhang

Yap

2012

Advances in Human-Computer Interaction

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We have developed an intelligent agent to engage with users in virtual drama improvisation previously. The intelligent agent was able to perform sentence-level affect detection from user inputs with strong emotional indicators. However, we noticed that many inputs with weak or no affect indicators also contain emotional implication but were regarded as neutral expressions by the previous interpretation. In this paper, we employ latent semantic analysis to perform topic theme detection and identify target audiences for such inputs. We also discuss how such semantic interpretation of the dialog contexts is used to interpret affect more appropriately during virtual improvisation. Also, in order to build a reliable affect analyser, it is important to detect and combine weak affect indicators from other channels such as body language. Such emotional body language detection also provides a nonintrusive channel to detect users' experience without interfering with the primary task. Thus, we also make initial exploration on affect detection from several universally accepted emotional gestures.

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“…To handle this gap, LSA model [7], such as probabilistic latent semantic index (pLSI) [8] and Latent Dirichlet Allocation (LDA) [9] have been proposed to extract lowdimensional semantic features. Although integrating extra semantic information is promising, this kind of approach only build linear and shallow models that can only capture pairwise semantic relationships between words [10].…”

Section: Introductionmentioning

confidence: 99%

Modeling Joint Representation with Tri-Modal Deep Belief Networks for Query and Question Matching

Jiang

Rong

Peng

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYOne of the main research tasks in community question answering (cQA) is finding the most relevant questions for a given new query, thereby providing useful knowledge for users. The straightforward approach is to capitalize on textual features, or a bag-of-words (BoW) representation, to conduct the matching process between queries and questions. However, these approaches have a lexical gap issue which means that, if lexicon matching fails, they cannot model the semantic meaning. In addition, latent semantic models, like latent semantic analysis (LSA), attempt to map queries to its corresponding semantically similar questions through a lower dimension representation. But alas, LSA is a shallow and linear model that cannot model highly non-linear correlations in cQA. Moreover, both BoW and semantic oriented solutions utilize a single dictionary to represent the query, question, and answer in the same feature space. However, the correlations between them, as we observe from data, imply that they lie in entirely different feature spaces. In light of these observations, this paper proposes a tri-modal deep belief network (tri-DBN) to extract a unified representation for the query, question, and answer, with the hypothesis that they locate in three different feature spaces. Besides, we compare the unified representation extracted by our model with other representations using the Yahoo! Answers queries on the dataset. Finally, Experimental results reveal that the proposed model captures semantic meaning both within and between queries, questions, and answers. In addition, the results also suggest that the joint representation extracted via the proposed method can improve the performance of cQA archives searching.

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Latent semantic analysis

Cited by 17 publications

References 6 publications

Performance impact of stop lists and morphological decomposition on word–word corpus-based semantic space models

Performance impact of stop lists and morphological decomposition on word–word corpus-based semantic space models

Affect Detection from Text-Based Virtual Improvisation and Emotional Gesture Recognition

Modeling Joint Representation with Tri-Modal Deep Belief Networks for Query and Question Matching

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