Word2Vec Model Analysis for Semantic Similarities in English Words

Bijaksana, Moch Arif; Suryani, Arie Ardiyanti

doi:10.1016/j.procs.2019.08.153

Cited by 137 publications

(72 citation statements)

References 3 publications

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“…These correlation scores can be found in Table 8 in the Appendix, they are all between .63 and .66. This is comparable to the correlations between .60 and .67 that were found using word2vec by Jatnika et al [2019], which is already better than the model trained by Google they used as comparison [Mikolov et al, 2013b]. As may be expected with a smaller corpus, the scores for the data set of new articles are slightly lower (between .59 and .64), but still reasonable.…”

Section: Methodssupporting

confidence: 75%

Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings

Schmahl¹,

Viering

Makrodimitris

et al. 2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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Large text corpora used for creating word embeddings (vectors which represent word meanings) often contain stereotypical gender biases. As a result, such unwanted biases will typically also be present in word embeddings derived from such corpora and downstream applications in the field of natural language processing (NLP). To minimize the effect of gender bias in these settings, more insight is needed when it comes to where and how biases manifest themselves in the text corpora employed. This paper contributes by showing how gender bias in word embeddings from Wikipedia has developed over time. Quantifying the gender bias over time shows that art related words have become more female biased. Family and science words have stereotypical biases towards respectively female and male words. These biases seem to have decreased since 2006, but these changes are not more extreme than those seen in random sets of words. Career related words are more strongly associated with male than with female, this difference has only become smaller in recently written articles. These developments provide additional understanding of what can be done to make Wikipedia more gender neutral and how important time of writing can be when considering biases in word embeddings trained from Wikipedia or from other text corpora.

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Section: Methodssupporting

confidence: 75%

Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings

Schmahl¹,

Viering

Makrodimitris

et al. 2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

View full text Add to dashboard Cite

show abstract

“…In the same direction, the study in [22] investigated the similarity of English words using the Word2Vec representation technique. The model was trained on English Wikipedia pages, and the cosine similarity method was employed for determining the similarity values.…”

Section: Utilization Of Word2vec In Osnsmentioning

confidence: 99%

Latent Twitter Image Information for Social Analytics

2021

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The appearance of images in social messages is continuously increasing, along with user engagement with that type of content. Analysis of social images can provide valuable latent information, often not present in the social posts. In that direction, a framework is proposed exploiting latent information from Twitter images, by leveraging the Google Cloud Vision API platform, aiming at enriching social analytics with semantics and hidden textual information. As validated by our experiments, social analytics can be further enriched by considering the combination of user-generated content, latent concepts, and textual data extracted from social images, along with linked data. Moreover, we employed word embedding techniques for investigating the usage of latent semantic information towards the identification of similar Twitter images, thereby showcasing that hidden textual information can improve such information retrieval tasks. Finally, we offer an open enhanced version of the annotated dataset described in this study with the aim of further adoption by the research community.

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“…Cosine similarity is a real number between -1 to 1. If the cosine similarity between two words is close to -1, then the words tend to have opposite meaning; if it is close to 1, they tend to have nearly the same meaning [30]. Equation 1shows the formula for cosine similarity, where W and X are word embedding vectors, S(W  , X  ) denotes the cosine similarity between W  and X  , k means for each word and n is the number of words embedded.…”

Section: B Cosine-similarity-based Abstractimilarity Using Word2vecmentioning

confidence: 99%

Word-Embedding-Based Traffic Document Classification Model for Detecting Emerging Risks Using Sentiment Similarity Weight

2020

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With the increase in traffic accident rates, traffic risk detection is becoming increasingly important. Moreover, it is necessary to provide appropriate traffic information considering user locations and routes and design an analysis method accordingly. This paper proposes a word-embedding-based traffic document classification model for detecting emerging risks using a quantity termed sentiment similarity weight (SSW). The proposed method detects emerging risks by considering and classifying the importance and polarity of keywords in traffic document. Conventional sentiment analysis methods fail to utilize semantically significant keywords unless they are included in a sentiment dictionary. In this study, through word imputation using an established similarity dictionary and by widening the limited utilization range, the proposed method overcomes the disadvantage of sentiment dictionaries. The proposed method is evaluated through three tests. In the first, the similarity between keywords is measured, and thus model accuracy is evaluated. In the second test, three classifiers for emerging risk classification are compared. In the last test, emerging risk detection is assessed according to whether the proposed SSW is applied, and its effectiveness is therefore verified. The evaluation results demonstrate that the proposed traffic-related document classification model using the SSW has an f-measure of 0.907, indicating satisfactory performance. Therefore, the proposed SSW can be effectively used as a parameter in traffic-related document classification and enables the detection of emerging risks.

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Word2Vec Model Analysis for Semantic Similarities in English Words

Cited by 137 publications

References 3 publications

Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings

Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings

Latent Twitter Image Information for Social Analytics

Word-Embedding-Based Traffic Document Classification Model for Detecting Emerging Risks Using Sentiment Similarity Weight

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