Задачи Сходства Слов

Vector-space word representations have been very successful in recent years at im- proving performance across a variety of NLP tasks. However, common to most existing work, words are regarded as …...

Distributed word representations have been widely used and proven to be useful in quite a few natural language processing and text mining tasks. Most of existing word embedding models aim …...

Word embeddings have been found useful for many NLP tasks, including part-of-speech tagging, named entity recognition, and pars- ing. Adding multilingual context when learn- ing embeddings can improve their quality, …...

Word embedding models learn vectorial word representations that can be used in a variety of NLP applications. When training data is scarce, these models risk losing their gener- alization abilities …...

AbstractRecent studies of distributional semantic models have set up a competition between word embeddings obtained from predictive neural networks and word vectors obtained from abstractive count-based models. This paper is …...

AbstractThis paper is concerned with identifying contexts useful for training word representation models for different word classes such as adjectives (A), verbs (V), and nouns (N). We introduce a simple …...

AbstractLearning representations of words in a continuous space is perhaps the most fundamental task in NLP, however words interact in ways much richer than vector dot product similarity can provide. …...