Comparative Analysis of Word Embeddings for Capturing Word Similarities
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
Code (1)
Tasks
Word EmbeddingsSimilar Papers 제목 키워드 기반
Assessing Wordnets with WordNet Embeddings
An effective conversion method was proposed in the literature to obtain a lexical semantic space from a lexical semantic graph, thus permitting to obtain WordNet embeddings from WordNets. In this paper, we propose the ex…
Semantic SimilaritySemantic Textual SimilarityWord EmbeddingsComparative Analysis of Static and Contextual Embeddings for Analyzing Semantic Changes in Medieval Latin Charters
The Norman Conquest of 1066 C.E. brought profound transformations to England's administrative, societal, and linguistic practices. The DEEDS (Documents of Early England Data Set) database offers a unique opportunity to e…
Word EmbeddingsWord Embeddings for the Analysis of Ideological Placement in Parliamentary Corpora
Word embeddings, the coefficients from neural network models predicting the use of words in context, have now become inescapable in applications involving natural language processing. Despite a few studies in political s…
Word EmbeddingsLayer-Wise Analysis of Self-Supervised Acoustic Word Embeddings: A Study on Speech Emotion Recognition
The efficacy of self-supervised speech models has been validated, yet the optimal utilization of their representations remains challenging across diverse tasks. In this study, we delve into Acoustic Word Embeddings (AWEs…
Emotion RecognitionSpeech Emotion RecognitionWord EmbeddingsLexicon Integrated CNN Models with Attention for Sentiment Analysis
With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting. This paper introduces a novel approach to sentim…
Sentiment AnalysisWord Embeddings