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 ...

Within the field of Natural Language Processing (NLP), the word2vec model has been extensively explored in the field of vector representation of words. It is a neural network that is based on the hypothesis that similar ...

Natural Language Processing has gone through substantial changes over time. It was only recently that statistical approaches started receiving attention. The Word2Vec model is one of these. It is a shallow neural network ...

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 ...

In this work, we propose a novel re-sampling method based on word lattice information and we use prosodic cues with support vector machines for classification. The idea is to consider word recognition as a two-class ...

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 ...

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 ...

Learning-based focused crawlers download relevant uniform resource locators (URLs) from the web for a specific topic. Several studies have used the term frequency-inverse document frequency (TF-IDF) weighted cosine vector ...