1,178 research outputs found
Handling Homographs in Neural Machine Translation
Homographs, words with different meanings but the same surface form, have
long caused difficulty for machine translation systems, as it is difficult to
select the correct translation based on the context. However, with the advent
of neural machine translation (NMT) systems, which can theoretically take into
account global sentential context, one may hypothesize that this problem has
been alleviated. In this paper, we first provide empirical evidence that
existing NMT systems in fact still have significant problems in properly
translating ambiguous words. We then proceed to describe methods, inspired by
the word sense disambiguation literature, that model the context of the input
word with context-aware word embeddings that help to differentiate the word
sense be- fore feeding it into the encoder. Experiments on three language pairs
demonstrate that such models improve the performance of NMT systems both in
terms of BLEU score and in the accuracy of translating homographs.Comment: NAACL201
Embeddings for word sense disambiguation: an evaluation study
Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, many tasks in Natural Language Processing have tried to take advantage of the potential of these distributional models. In this work, we study how word embeddings can be used in Word Sense Disambiguation, one of the oldest tasks in Natural Language Processing and Artificial Intelligence. We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance. We show how a WSD system that makes use of word embeddings alone, if designed properly, can provide significant performance improvement over a state-of-the-art WSD system that incorporates several standard WSD features
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
Researc
Syntax-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 layers are jointly learned against a generic objective that
enhances the vectors with syntactic information from the surrounding context.
Furthermore, each word is associated with a number of senses, the most
plausible of which is selected dynamically during the composition process. We
evaluate the produced vectors qualitatively and quantitatively with positive
results. At the sentence level, the effectiveness of the framework is
demonstrated on the MSRPar task, for which we report results within the
state-of-the-art range.Comment: Accepted for presentation at EMNLP 201
Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation
This paper demonstrates that word sense disambiguation (WSD) can improve
neural machine translation (NMT) by widening the source context considered when
modeling the senses of potentially ambiguous words. We first introduce three
adaptive clustering algorithms for WSD, based on k-means, Chinese restaurant
processes, and random walks, which are then applied to large word contexts
represented in a low-rank space and evaluated on SemEval shared-task data. We
then learn word vectors jointly with sense vectors defined by our best WSD
method, within a state-of-the-art NMT system. We show that the concatenation of
these vectors, and the use of a sense selection mechanism based on the weighted
average of sense vectors, outperforms several baselines including sense-aware
ones. This is demonstrated by translation on five language pairs. The
improvements are above one BLEU point over strong NMT baselines, +4% accuracy
over all ambiguous nouns and verbs, or +20% when scored manually over several
challenging words.Comment: To appear in TAC
BIOMEDICAL WORD SENSE DISAMBIGUATION WITH NEURAL WORD AND CONCEPT EMBEDDINGS
Addressing ambiguity issues is an important step in natural language processing (NLP) pipelines designed for information extraction and knowledge discovery. This problem is also common in biomedicine where NLP applications have become indispensable to exploit latent information from biomedical literature and clinical narratives from electronic medical records. In this thesis, we propose an ensemble model that employs recent advances in neural word embeddings along with knowledge based approaches to build a biomedical word sense disambiguation (WSD) system. Specifically, our system identities the correct sense from a given set of candidates for each ambiguous word when presented in its context (surrounding words). We use the MSH WSD dataset, a well known public dataset consisting of 203 ambiguous terms each with nearly 200 different instances and an average of two candidate senses represented by concepts in the unified medical language system (UMLS). We employ a popular biomedical concept, Our linear time (in terms of number of senses and context length) unsupervised and knowledge based approach improves over the state-of-the-art methods by over 3% in accuracy. A more expensive approach based on the k-nearest neighbor framework improves over prior best results by 5% in accuracy. Our results demonstrate that recent advances in neural dense word vector representations offer excellent potential for solving biomedical WSD
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