We describe an algorithm that combines lexical information (from WordNet 1.7) with Web directories (from the Open Directory Project) to associate word senses with such directories. Such associations can be used as rich characterizations to acquire sense-tagged corpora automatically, cluster topically-related senses and detect sense specializations. The algorithm is evaluated for the 29 nouns (1...

This paper presents the task definition, resources, participating systems, and comparative results for the shared task on word alignment, which was organized as part of the ACL 2005 Workshop on Building and Using Parallel Texts. The shared task included English–Inuktitut, Romanian–English, and English–Hindi sub-tasks, and drew the participation of ten teams from around the world with a total of...

This paper reports on the word sense disambiguation of Korean noun by using co-occurrence information in context. For a given noun, its local contextual word distribution is not enough to express their semantic characteristics for noun sense disambiguation. This paper proposes a cluster-based sense as a base vector. Contextual noise is removed by a term weighting method, and hypernyms of remain...

Current Word Sense Disambiguation systems show an extremely poor performance on low frequent senses, which is mainly caused by the difference in sense distributions between training and test data. The main focus in tackling this problem has been on acquiring more data or selecting a single predominant sense and not necessarily on the meta properties of the data itself. We demonstrate that these...

Recently, Yuan et al. (2016) have shown the effectiveness of using Long ShortTerm Memory (LSTM) for performing Word Sense Disambiguation (WSD). Their proposed technique outperformed the previous state-of-the-art with several benchmarks, but neither the training data nor the source code was released. This paper presents the results of a reproduction study of this technique using only openly avai...

Word Sense Disambiguation (WSD) is one of the key issues in natural language processing. Currently, supervised WSD methods are effective ways to solve the ambiguity problem. However, due to lacking of large-scale training data, they cannot achieve satisfactory results. In this paper, we suppose synonyms for context words that can provide more knowledge for WSD task, and present two different WS...

This paper shows that our WSD system using rich linguistic features achieved high accuracy in the classification of English SENSEVAL2 verbs for both fine-grained (64.6%) and coarse-grained (73.7%) senses. We describe three specific enhancements to our treatment of rich linguistic features and present their separate and combined contributions to our system’s performance. Further experiments show...

Word Sense Disambiguation (WSD) is one of the key issues in natural language processing. Currently, supervised WSD methods are effective ways to solve the ambiguity problem. However, due to lacking of large-scale training data, they cannot achieve satisfactory results. In this paper, we present a WSD method based on context translation. The method is based on the assumption that translation und...

The current situation for Word Sense Disambiguation (WSD) is somewhat stuck due to lack of training data. We present in this paper a novel disambiguation algorithm that improves previous systems based on acquisition of examples by incorporating local context information. With a basic configuration, our method is able to obtain state-of-the-art performance. We complemented this work by evaluatin...