Models that represent meaning as high-dimensional numerical vectors—such as latent
semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the …

We propose two novel model architectures for computing continuous vector representations
of words from very large data sets. The quality of these representations is measured in a …

Recent work has shown that neuralembedded word representations capture many relational
similarities, which can be recovered by means of vector arithmetic in the embedded space …

Many machine learning algorithms require the input to be represented as a fixed length
feature vector. When it comes to texts, one of the most common representations is bag-of …

Artificial Intelligence federates numerous scientific fields in the aim of developing machines
able to assist human operators performing complex treatments---most of which demand high …

Word embeddings have recently gained considerable popularity for modeling words in
different Natural Language Processing (NLP) tasks including semantic similarity …

In this paper, we study the answer sentence selection problem for question answering.
Unlike previous work, which primarily leverages syntactic analysis through dependency tree …

Recent work on word embeddings has shown that simple vector subtraction over pre-trained
embeddings is surprisingly effective at capturing different lexical relations, despite lacking …

We introduce HyperLex—a data set and evaluation resource that quantifies the extent of the
semantic category membership, that is, type-of relation, also known as hyponymy …

Automatic short-answer grading has been studied for more than a decade. The technique
has been used for implementing auto assessment as well as building the assessor module …