The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text

Altszyler, Edgar; Ribeiro, Sidarta; Sigman, Mariano; Slezak, Diego Fernández

doi:10.1016/j.concog.2017.09.004

Cited by 61 publications

(36 citation statements)

References 45 publications

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“…We discarded infrequent tokens, with less than 5 repetitions and very frequent tokens, with a frequency higher than 10 −3 . We set the window size and negative sampling to 15 (which were found to be maximal in two semantic tasks over TASA corpus (Altszyler et al, 2017)). Word2vec semantic representations were generated with the Gensim Python library (Rehuek and Sojka, 2010).…”

Section: Word2vec Representation (50 Features)mentioning

confidence: 99%

Using contextual information for automatic triage of posts in a peer-support forum

Altszyler¹,

Berenstein²,

Milne³

et al. 2018

Proceedings of the Fifth Workshop on Computational Linguistics And Clinical Psychology: From Keyboard to Clinic

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Mental health forums are online spaces where people can share their experiences anonymously and get peer support. These forums, require the supervision of moderators to provide support in delicate cases, such as posts expressing suicide ideation. The large increase in the number of forum users makes the task of the moderators unmanageable without the help of automatic triage systems. In the present paper, we present a Machine Learning approach for the triage of posts. Most approaches in the literature focus on the content of the posts, but only a few authors take advantage of features extracted from the context in which they appear. Our approach consists of the development and implementation of a large variety of new features from both, the content and the context of posts, such as previous messages, interaction with other users and author's history. Our method has competed in the CLPsych 2017 Shared Task, obtaining the first place for several of the subtasks. Moreover, we also found that models that take advantage of post context improve significantly its performance in the detection of flagged posts (posts that require moderators attention), as well as those that focus on post content outperforms in the detection of most urgent events.

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Section: Word2vec Representation (50 Features)mentioning

confidence: 99%

Using contextual information for automatic triage of posts in a peer-support forum

Altszyler¹,

Berenstein²,

Milne³

et al. 2018

Proceedings of the Fifth Workshop on Computational Linguistics And Clinical Psychology: From Keyboard to Clinic

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“…In LSA implementation, a Log-Entropy transformation was applied before the truncated Singular Value Decomposition. In Skip-gram implementation, we discarded tokens with frequency higher than 10 −3 , and we set the window size and negative sampling parameters to 15 (which were found to be maximal in two semantic tasks over TASA corpus (Altszyler et al, 2017)). In all cases, word-embeddings dimensions values were varied to study its dependency.…”

Section: Methodsmentioning

confidence: 99%

Corpus Specificity in LSA and Word2vec: The Role of Out-of-Domain Documents

Altszyler

Sigman

Slezak

2018

Proceedings of the Third Workshop on Representation Learning for NLP

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Latent Semantic Analysis (LSA) and Word2vec are some of the most widely used word embeddings. Despite the popularity of these techniques, the precise mechanisms by which they acquire new semantic relations between words remain unclear. In the present article we investigate whether LSA and Word2vec capacity to identify relevant semantic dimensions increases with size of corpus. One intuitive hypothesis is that the capacity to identify relevant dimensions should increase as the amount of data increases. However, if corpus size grow in topics which are not specific to the domain of interest, signal to noise ratio may weaken. Here we set to examine and distinguish these alternative hypothesis. To investigate the effect of corpus specificity and size in word-embeddings we study two ways for progressive elimination of documents: the elimination of random documents vs. the elimination of documents unrelated to a specific task. We show that Word2vec can take advantage of all the documents, obtaining its best performance when it is trained with the whole corpus. On the contrary, the specialization (removal of out-ofdomain documents) of the training corpus, accompanied by a decrease of dimensionality, can increase LSA word-representation quality while speeding up the processing time. Furthermore, we show that the specialization without the decrease in LSA dimensionality can produce a strong performance reduction in specific tasks. From a cognitive-modeling point of view, we point out that LSA's word-knowledge acquisitions may not be efficiently exploiting higher-order co-occurrences and global relations, whereas Word2vec does.

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“…A recent use of LDA and word2vec include detection of fake news on Twitter (Helmstetter & Paulheim, 2018). For other examples of uses of the LSA and LDA algorithms in a psychological context, we refer the reader to Chen and Wojcik (2016) and Altszyler, Ribeiro, Sigman, and Slezak (2017).…”

Section: Textual Datamentioning

confidence: 99%

Advances in Machine Learning for the Behavioral Sciences

Kliegr

Bahník

Fürnkranz

2019

American Behavioral Scientist

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The areas of machine learning and knowledge discovery in databases have considerably matured in recent years. In this article, we briefly review recent developments as well as classical algorithms that stood the test of time. Our goal is to provide a general introduction into different tasks such as learning from tabular data, behavioral data, or textual data, with a particular focus on actual and potential applications in behavioral sciences. The supplemental appendix to the article also provides practical guidance for using the methods by pointing the reader to proven software implementations. The focus is on R, but we also cover some libraries in other programming languages as well as systems with easy-to-use graphical interfaces.

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The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text

Cited by 61 publications

References 45 publications

Using contextual information for automatic triage of posts in a peer-support forum

Using contextual information for automatic triage of posts in a peer-support forum

Corpus Specificity in LSA and Word2vec: The Role of Out-of-Domain Documents

Advances in Machine Learning for the Behavioral Sciences

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