An experiment was performed at the National Library of Medicine ® (NLM ® ) in word sense disambiguation (WSD) using the Journal Descriptor Indexing (JDI) methodology. The motivation is the need to solve the ambiguity problem confronting NLM’s MetaMap system, which maps free text to terms corresponding to concepts in NLM’s Unified Medical Language System ® (UMLS ® ) Metathesaurus ®. If the text maps to more than one Metathesaurus concept at the same high confidence score, MetaMap has no way of knowing which concept is the correct mapping. We describe the JDI methodology, which is ultimately based on statistical associations between words in a training set of MEDLINE ® citations and a small set of journal descriptors (assigned by humans to journals per se) assumed to be inherited by the citations. JDI is the

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SenseClusters is a freely available word sense discrimination system that takes a purely unsupervised clustering approach. It uses no knowledge other than what is available in a raw unstructured corpus, and clusters instances of a given target word based only on their mutual contextual similarities. It is a complete system that provides support for feature selection from large corpora, several different context representation schemes, various clustering algorithms, and evaluation of the discovered clusters.

This paper investigates conceptually and empirically the novel sense matching task, which requires to recognize whether the senses of two synonymous words match in context. We suggest direct approaches to the problem, which avoid the intermediate step of explicit word sense disambiguation, and demonstrate their appealing advantages and stimulating potential for future research. 1

SenseClusters is a freely available system that identifies similar contexts in text. It relies on lexical features to build first and second order representations of contexts, which are then clustered using unsupervised methods. It was originally developed to discriminate among contexts centered around a given target word, but can now be applied more generally. It also supports methods that create descriptive and discriminating labels for the discovered clusters. 1

The paper compares the tasks of part-of-speech (POS) tagging and word-sense-tagging or disambiguation (WSD), and argues that the tasks are not related by fineness of grain or anything like that, but are quite different kinds of task, particularly because there is nothing in POS corresponding to sense novelty. The paper also argues for the reintegration of sub-tasks that are being separated for evaluation.

Abstract. Words with multiple meanings are a phenomenon inherent to any natural language. In this work, we study the effects of such lexical ambiguities on second language vocabulary learning. We demonstrate that machine learning algorithms for word sense disambiguation can induce classifiers that exhibit high accuracy at the task of disambiguating homonyms (words with multiple distinct meanings). Results from a user study that compared two versions of a vocabulary tutoring system, one that applied word sense disambiguation to support learning and another that did not, support rejection of the null hypothesis that learning outcomes with and without word sense disambiguation are equivalent, with a p-value of 0.001. To our knowledge this is the first work that investigates the efficacy of word sense disambiguation for facilitating second language vocabulary learning.

This chapter focuses on unsupervised corpus-based methods of word sense discrimination that are knowledge-lean, and do not rely on external knowledge sources such as machine readable dictionaries, concept hierarchies, or sense-tagged text. They do not assign sense tags to words; rather, they discriminate among word meanings based on information found in unannotated corpora. This chapter reviews distributional approaches that rely on monolingual corpora and methods based on translational equivalence as found in word-aligned parallel corpora. These techniques are organized into type- and token-based approaches. The former identify sets of related words, while the latter distinguish among the senses of a word used in multiple contexts.

This paper presents an unsupervised word sense learning algorithm, which induces senses of target word by grouping its occurrences into a “natural” number of clusters based on the similarity of their contexts. For removing noisy words in feature set, feature selection is conducted by optimizing a cluster validation criterion subject to some constraint in an unsupervised manner. Gaussian mixture model and Minimum Description Length criterion are used to estimate cluster structure and cluster number. Experimental results show that our algorithm can find important feature subset, estimate model order (cluster number) and achieve better performance than another algorithm which requires cluster number to be provided. 1

learning algorithms on word sense disambiguation with small datasets