Word embedding approaches increased the efficiency of natural language processing (NLP) tasks. Traditional word embeddings though robust for many NLP activities, do not handle polysemy of words. The tasks of semantic ...

This work explores the use of word embeddings as features for Spanish verb sense disambiguation (VSD). This type of learning technique is named disjoint semisupervised learning [21]: an unsupervised algorithm (i.e. the ...

Los word embeddings, también denominados representaciones vectoriales de palabras, son vectores de números reales, de pocas dimensiones, los cuales son utilizados en la resolución de distintas tareas relacionadas al ...

Traditional word embeddings approaches, such as bag-of-words models, tackles the problem of text data representation by linking words in a document to a binary vector, marking their occurrence or not. Additionally, a term ...

Models of word embeddings are often assessed when solving syntactic and semantic analogies. Among the latter, we are interested in relations that one would find in lexical-semantic knowledge bases like WordNet, also covered ...

Word embeddings have become essential components in various information retrieval and natural language processing tasks, such as ranking, document classification, and question answering. However, despite their widespread ...

Numerical vector representations are able to represent from words to meanings, in a low-dimensional continuous space. These representations are based on distributional modeling, where the context in which the word occurs ...

Background: Part-of-speech tagging is an important preprocessing step in many natural language processing applications. Despite much work already carried out in this field, there is still room for improvement, especially ...