Publication

Explicit Suggestion of Query Terms for News Search using Topic Models and Word Embeddings

2016
Reports

Abstract

This report presents a study on assisting users in building queries to perform real-time searches in a news and social media monitoring system. The system accepts complex queries, and we assist the user by suggesting related keywords or entities. We do this by leveraging two different word representations: (1) probabilistic topic models, and (2) unsupervised word embeddings. We compare the vector representations obtained by these two approaches to find related keywords (i.e. suggestions) with respect to specific queries, taken from the query log of a commercial system. Through crowdsourcing we solicited relevance judgments and compared the two methods. Our results show that word embeddings outperform topic models for keyword suggestion.

Official source

https://infoscience.epfl.ch/entities/publication/0483be07-a419-4e54-8e7d-ce2cb8007542

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2016
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/0483be07-a419-4e54-8e7d-ce2cb8007542

About this result

Related concepts (32)

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.

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.

Word2vec

Word2vec is a technique for natural language processing (NLP) published in 2013. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. As the name implies, word2vec represents each distinct word with a particular list of numbers called a vector.

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