sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings (1511.06388v1)

Abstract: Neural word representations have proven useful in NLP tasks due to their ability to efficiently model complex semantic and syntactic word relationships. However, most techniques model only one representation per word, despite the fact that a single word can have multiple meanings or "senses". Some techniques model words by using multiple vectors that are clustered based on context. However, recent neural approaches rarely focus on the application to a consuming NLP algorithm. Furthermore, the training process of recent word-sense models is expensive relative to single-sense embedding processes. This paper presents a novel approach which addresses these concerns by modeling multiple embeddings for each word based on supervised disambiguation, which provides a fast and accurate way for a consuming NLP model to select a sense-disambiguated embedding. We demonstrate that these embeddings can disambiguate both contrastive senses such as nominal and verbal senses as well as nuanced senses such as sarcasm. We further evaluate Part-of-Speech disambiguated embeddings on neural dependency parsing, yielding a greater than 8% average error reduction in unlabeled attachment scores across 6 languages.

An Expert Overview of the Sense2vec Model for Word Sense Disambiguation in NLP

The paper "sense2vec - a fast and accurate method for word sense disambiguation in neural word embeddings" presents a notable advancement in the domain of NLP by addressing the longstanding challenge of word sense disambiguation within neural word embeddings. This paper is particularly relevant to researchers focusing on optimizing the utility of word embeddings in NLP applications.

Problem Context and Objectives

Traditional word embedding techniques, such as word2vec and its variants, typically encapsulate all potential meanings of a word into a singular vector representation. This conflation creates a "superposition" of meanings, complicating the contextual understanding necessary for precise NLP tasks. Past attempts to mitigate this issue involved clustering methodologies to differentiate word senses, but these approaches often suffer from computational inefficiencies and lack direct applicability to NLP algorithms.

The authors propose sense2vec, a model that departs from unsupervised clustering by employing supervised disambiguation using NLP labels. This methodology allows for the generation of multiple embeddings per word, facilitating the accurate and computationally efficient realization of word senses.

Methodological Insights

Sense2vec leverages labeled corpora, assigning distinct embeddings for each word sense through models structured around well-known configurations like CBOW and Skip-gram. Crucially, these embeddings predict word senses in their given contexts rather than isolated tokens. This innovative approach aims to resolve the ambiguities stemming from polysemy and improve model performance on subsequent NLP tasks.

The paper's empirical evaluations attest to sense2vec's effectiveness in several domains including Part-of-Speech (POS) tagging, sentiment analysis, named entity recognition (NER), and syntactic dependency parsing. Notable success is achieved in syntactic contexts across six languages with a reported mean error reduction of over 8% in unlabeled attachment scores, illustrating the model's robustness and multilingual applicability.

Key Results and Contributions

The subjective evaluations presented in the paper demonstrate sense2vec's capacity to separate word senses based on POS and sentiment, showcasing its potential for real-world applications such as sentiment analysis, where differentiating between sarcastic and literal meanings of the same word (e.g., "bad") is crucial. Similarly, named entity resolution results underscore the model's ability to effectively distinguish between entities like "Washington" as both a PERSON and a GPE.

Additionally, the authors emphasize that sense2vec reduces the computational overhead inherent in previous models by eliminating the need for repeated training and clustering steps. The model offers a streamlined process that instinctively aligns with supervised NLP tasks, ensuring that disambiguation is more seamlessly integrated into broader NLP systems.

Implications and Future Research

The findings suggest that sense2vec has significant implications for improving the accuracy and efficiency of various NLP applications. Its architectural simplicity paired with substantial efficacy advocates for broader application in diverse linguistic and contextual scenarios. As stated in the conclusion, future research directions might include exploring the impact of alternative supervised labels on sense2vec’s performance and its integration into even more complex NLP models and tasks.

In summary, this paper contributes a technically sound and empirically validated model to the landscape of word sense disambiguation, potentially setting a new standard for integrating semantic understanding within neural embeddings in NLP systems. Researchers and practitioners in the field should consider the sense2vec model as a viable option for enhancing word sense clarity in their NLP pipelines.