Dimensionality Prediction for Word Embeddings

Sindhu, Korrapati; Seshadri, Karthick

doi:10.1007/978-981-33-4604-8_24

Algorithms for Intelligent Systems

2021

DOI: 10.1007/978-981-33-4604-8_24

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Dimensionality Prediction for Word Embeddings

Korrapati Sindhu

Karthick Seshadri

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2024

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Automatic Topic Title Assignment with Word Embedding

Zammarchi,

Romano,

Conversano

2024

J Classif

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In this paper, we propose TAWE (title assignment with word embedding), a new method to automatically assign titles to topics inferred from sets of documents. This method combines the results obtained from the topic modeling performed with, e.g., latent Dirichlet allocation (LDA) or other suitable methods and the word embedding representation of words in a vector space. This representation preserves the meaning of the words while allowing to find the most suitable word that represents the topic. The procedure is twofold: first, a cleaned text is used to build the LDA model to infer a desirable number of latent topics; second, a reasonable number of words and their weights are extracted from each topic and represented in n-dimensional space using word embedding. Based on the selected weighted words, a centroid is computed, and the closest word is chosen as the title of the topic. To test the method, we used a collection of tweets about climate change downloaded from some of the main newspapers accounts on Twitter. Results showed that TAWE is a suitable method for automatically assigning a topic title.

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Automatic Topic Title Assignment with Word Embedding

Zammarchi,

Romano,

Conversano

2024

J Classif

View full text Add to dashboard Cite

show abstract

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Dimensionality Prediction for Word Embeddings

Cited by 1 publication

References 15 publications

Automatic Topic Title Assignment with Word Embedding

Automatic Topic Title Assignment with Word Embedding

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