Word embeddings have attracted much attention in recent years and have been heavily applied to many tasks in information retrieval, natural language processing and knowledge base construction. One of the most well noted aspects of word embeddings is their ability to capture relations between terms via simple vector offsets. This ability is often examined via the use of proportional analogy completion tasks. This task requires that the correct single term be returned by a system when prompted with the three other terms of a proportional analogy. This task usually involves a scan of all stored word embeddings which may be a relatively expensive operation when used as part of a larger system. In some preliminary experiments we show that it is ...
Word embeddings, which represent words as dense feature vectors, are widely used in natural language...
We observe that thus far all computational models of analogy have modelled memory as a set of disjoi...
Abstract. We present an algorithm for learning from unlabeled text, based on the Vector Space Model ...
It has been argued that analogy is the core of cognition. In AI research, algorithms for analogy are...
Recent trends suggest that neural-network-inspired word embedding models outperform traditional coun...
Analogical proportions are statements of the form ‘a is to b as c is to d’, formally denoted a:b::c:...
Recent work has shown that neural-embedded word representations capture many relational similarities...
Recent work has shown that neural-embedded word representations capture many relational similarities...
Requests for recommendation can be seen as a form of query for candidate items, ranked by relevance....
How does the word analogy task fit in the modern NLP landscape? Given the rarity of comparable multi...
International audienceIn this paper we discuss the well-known claim that language analogies yield al...
Analogies are a fundamental human reasoning pattern that relies on relational similarity. Understand...
Discovering synonyms and other related words among the words in a document collection can be seen as...
The present work examined subjects' performance on eight types of four word analogy problems. Two cr...
Methods for representing the meaning of words in vector spaces purely using the information distribu...
Word embeddings, which represent words as dense feature vectors, are widely used in natural language...
We observe that thus far all computational models of analogy have modelled memory as a set of disjoi...
Abstract. We present an algorithm for learning from unlabeled text, based on the Vector Space Model ...
It has been argued that analogy is the core of cognition. In AI research, algorithms for analogy are...
Recent trends suggest that neural-network-inspired word embedding models outperform traditional coun...
Analogical proportions are statements of the form ‘a is to b as c is to d’, formally denoted a:b::c:...
Recent work has shown that neural-embedded word representations capture many relational similarities...
Recent work has shown that neural-embedded word representations capture many relational similarities...
Requests for recommendation can be seen as a form of query for candidate items, ranked by relevance....
How does the word analogy task fit in the modern NLP landscape? Given the rarity of comparable multi...
International audienceIn this paper we discuss the well-known claim that language analogies yield al...
Analogies are a fundamental human reasoning pattern that relies on relational similarity. Understand...
Discovering synonyms and other related words among the words in a document collection can be seen as...
The present work examined subjects' performance on eight types of four word analogy problems. Two cr...
Methods for representing the meaning of words in vector spaces purely using the information distribu...
Word embeddings, which represent words as dense feature vectors, are widely used in natural language...
We observe that thus far all computational models of analogy have modelled memory as a set of disjoi...
Abstract. We present an algorithm for learning from unlabeled text, based on the Vector Space Model ...