Publication

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Martin Jaggi, Matteo Pagliardini, Prakhar Gupta
2017
Reports

Abstract

The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simple but efficient unsupervised objective to train distributed representations of sentences. Our method outperforms the state-of-the-art unsupervised models on most benchmark tasks, highlighting the robustness of the produced general-purpose sentence embeddings.

Official source

https://infoscience.epfl.ch/entities/publication/a31b7565-9769-4692-b915-7b377b5ed457

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Martin Jaggi, Matteo Pagliardini, Prakhar Gupta
2017
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/a31b7565-9769-4692-b915-7b377b5ed457

About this result

Related concepts (22)

Word embedding

In natural language processing (NLP), a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that words that are closer in the vector space are expected to be similar in meaning. Word embeddings can be obtained using language modeling and feature learning techniques, where words or phrases from the vocabulary are mapped to vectors of real numbers.

Feature learning

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

Word-sense disambiguation

Word-sense disambiguation (WSD) is the process of identifying which sense of a word is meant in a sentence or other segment of context. In human language processing and cognition, it is usually subconscious/automatic but can often come to conscious attention when ambiguity impairs clarity of communication, given the pervasive polysemy in natural language. In computational linguistics, it is an open problem that affects other computer-related writing, such as discourse, improving relevance of search engines, anaphora resolution, coherence, and inference.

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Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

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