One Million Sense-Tagged Instances for Word Sense Disambiguation and Induction

Abstract

Supervised word sense disambiguation (WSD) systems are usually the best performing systems when evaluated on standard benchmarks. However, these systems need annotated training data to function properly. While there are some publicly available open source WSD systems, very few large annotated datasets are available to the research community. The two main goals of this paper are to extract and annotate a large number of samples and release them for public use, and also to evaluate this dataset against some word sense disambiguation and induction tasks. We show that the open source IMS WSD system trained on our dataset achieves stateof-the-art results in standard disambiguation tasks and a recent word sense induction task, outperforming several task submissions and strong baselines.

Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison

“…These approaches have shown the potential of using word embeddings on the WSD task. Iacobacci et al (2016) carried 7 As already noted by Taghipour and Ng (2015a), supervised systems trained on only OMSTI obtain lower results than when trained along with SemCor, mainly due to OM-STI's lack of coverage in target word types. 8 We used the original implementation available at http: //www.comp.nus.edu.sg/˜nlp/software.html out a comparison of different strategies for integrating word embeddings as a feature in WSD.…”

“…• OMSTI (Taghipour and Ng, 2015a OMSTI 5 has already shown its potential as a training corpus by improving the performance of supervised systems which add it to existing training data (Taghipour and Ng, 2015a;Iacobacci et al, 2016). Table 1 shows some statistics 6 of the WSD datasets and training corpora which we use in the evaluation framework.…”

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

“…SemCor (Miller et al, 1993) and OMSTI (Taghipour and Ng, 2015a). As sense inventory, we used WordNet 3.0 (Miller et al, 1990) for all open-class parts of speech.…”

Train-O-Matic: Large-Scale Supervised Word Sense Disambiguation in Multiple Languages without Manual Training Data

“…• One Million Sense-Tagged Instances (Taghipour and Ng, 2015, OMSTI), a sense-annotated dataset obtained via a semi-automatic approach based on the disambiguation of a parallel corpus, i.e., the United Nations Parallel Corpus, performed by exploiting manually translated word senses. Because OMSTI integrates SemCor to increase coverage, to keep a level playing field we excluded the latter from the corpus.…”