Sentence examples for vector of words from inspiring English sources

In our proposed hybrid RNNLM, a hybrid vector of words and PMs is used as an input vector.

Based on the word vector of words, we try to get similar nouns who have similar meanings.

The PLSA model maps the high-dimensional vector of words of a document to a lower dimensional vector of topics.

An RNNLM can model not only information from multiple-type input units through a hybrid input vector of words and PMs, but can also capture long context history through recurrent connections.

The cleaned string is then split into a vector of words, using white space and punctuation marks as delimiters.

The input vector of our proposed hybrid RNNLM is a concatenated vector of word and PM vectors.

A concatenated vector of word and PM vectors, or a hybrid vector, is used as an input vector instead of a word vector in the proposed hybrid RNNLM framework.

The oligonucleotide signature is a | Σ | k -dimensional vector of word frequencies.

It first constructs a vocabulary from the training text data and then learns vector representation of words, upon which the word vector file can be used as features in many Natural Language Processing (NLP) and machine learning applications.

Word embedding creates a vector representation of words with a much lower dimensional space compared to the bag of the words model [53, 54].

Word2vec uses a large-scale text corpus as input and produces vector representations of words by constructing a vocabulary and learning how to describe words outside the vocabulary from those inside it [8].