From Word To Sense Embeddings: A Survey on Vector Representations of Meaning
نویسندگان
چکیده
منابع مشابه
Learning Word Sense Embeddings from Word Sense Definitions
Word embeddings play a significant role in many modern NLP systems. Since learning one representation per word is problematic for polysemous words and homonymous words, researchers propose to use one embedding per word sense. Their approaches mainly train word sense embeddings on a corpus. In this paper, we propose to use word sense definitions to learn one embedding per word sense. Experimenta...
متن کاملWord Embeddings, Sense Embeddings and their Application to Word Sense Induction
This paper investigates the cutting-edge techniques for word embedding, sense embedding, and our evaluation results on large-scale datasets. Word embedding refers to a kind of methods that learn a distributed dense vector for each word in a vocabulary. Traditional word embedding methods first obtain the co-occurrence matrix then perform dimension reduction with PCA. Recent methods use neural la...
متن کاملMaking Sense of Word Embeddings
We present a simple yet effective approach for learning word sense embeddings. In contrast to existing techniques, which either directly learn sense representations from corpora or rely on sense inventories from lexical resources, our approach can induce a sense inventory from existing word embeddings via clustering of ego-networks of related words. An integrated WSD mechanism enables labeling ...
متن کاملJoint Learning of Sense and Word Embeddings
Methods for learning lower-dimensional representations (embeddings) of words using unlabelled data have received a renewed interested due to their myriad success in various Natural Language Processing (NLP) tasks. However, despite their success, a common deficiency associated with most word embedding learning methods is that they learn a single representation for a word, ignoring the different ...
متن کاملSyntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP research. We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence. Embeddings and composition l...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2018
ISSN: 1076-9757
DOI: 10.1613/jair.1.11259