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

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

MUSE: Modularizing Unsupervised Sense Embeddings

This paper proposes to address the word sense ambiguity issue in an unsupervised manner, where word sense representations are learned along a word sense selection mechanism given contexts. Prior work focused on designing a single model to deliver both mechanisms, and thus suffered from either coarse-grained representation learning or inefficient sense selection. The proposed modular approach, MUSE, implements flexible modules to optimize distinct mechanisms, achieving the first purely sense-level representation learning system with linear-time sense selection. We leverage reinforcement learning to enable joint training on the proposed modules, and introduce various exploration techniques on sense selection for better robustness. The experiments on benchmark data show that the proposed approach achieves the state-of-the-art performance on synonym selection as well as on contextual word similarities in terms of MaxSimC

Unsupervised Machine Learning Approach for Tigrigna Word Sense Disambiguation

All human languages have words that can mean different things in different contexts. Word sense disambiguation (WSD) is an open problem of natural language processing, which governs the process of identifying which sense of a word (i.e. meaning) is used in a sentence, when the word has multiple meanings (polysemy). We use unsupervised machine learning techniques to address the problem of automatically deciding the correct sense of an ambiguous word Tigrigna texts based on its surrounding context. And we report experiments on four selected Tigrigna ambiguous words due to lack of sufficient training data; these are መደብ read as “medeb” has three different meaning (Program, Traditional bed and Grouping), ሓለፈ read as “halefe”; has four dissimilar meanings (Pass, Promote, Boss and Pass away), ሃደመ read as “hademe”; has two different meaning (Running and Building house) and, ከበረ read as “kebere”; has two different meaning (Respecting and Expensive).Finally we tested five clustering algorithms (simple k means, hierarchical agglomerative: Single, Average and complete link and Expectation Maximization algorithms) in the existing implementation of Weka 3.8.1 package. “Use training set” evaluation mode was selected to learn the selected algorithms in the preprocessed dataset. We have evaluated the algorithms for the four ambiguous words and achieved the best accuracy within the range of 67 to 83.3 for EM which is encouraging result. Keywords: Attribute- Relation File Format, Cross Validation, Consonant Vowel, Machine Readable Dictionary, Natural Language Processing, System for Ethiopic Representation in ASCII, Word Sense Disambiguatio