We propose a promising neural network model with which to acquire grounded representation of robot actions and the linguistic descriptions thereof. Properly responding various expressions, including polysemous words, is an important ability for robots that interact people via dialogue. Previous studies have shown can use words are not included in action-description paired datasets by using pre-...

This article discusses Basilex, a 13.5-million tokens, 11.5-million Dutch words corpus of written language offered to children in the elementary school age, which was recently finalized. The corpus is automatically analyzed at the levels of part-of-speech tagging and lemmatization, and a limited amount of polysemous words has been partly automatically disambiguated. Also, a lemma-based lexicon ...

In this paper Schapire and Singer s AdaBoost MH boosting algorithm is applied to the Word Sense Disambiguation WSD problem Initial experiments on a set of selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar based ap proaches which represent state of the art accuracy on supervised WSD In order to make boosting practical for a real learning domain of thou ...

This paper considers the problem of approximate nearest neighbor search in the compressed domain. We introduce polysemous codes, which offer both the distance estimation quality of product quantization and the efficient comparison of binary codes with Hamming distance. Their design is inspired by algorithms introduced in the 90’s to construct channel-optimized vector quantizers. At search time,...

Although researches have been conducted on the polysemous nature of some Korean psych-adjectives, no consensus has been made on the criteria used for evaluating the polysemy. Furthermore, few formalizations (semantic structures) have been proposed for the polysemous phenomena. The purpose of this paper is twofold: 1) to propose new criteria for distinguishing polysemous psych-adjectives from mo...

Most words in natural languages are polysemous; that is, they have related but different meanings contexts. This one-to-many mapping of form to meaning presents a challenge understanding how word learned, represented, and processed. Previous work has focused on solutions which multiple static semantic representations linked single form, fails capture important generalizations about polysemous u...

Polysemy is one of the major causes of difficulties in selnantic clustering of words in a corpus. In this paper, we first; give a definition of polysemy from the viewpoint of clustering and then, b~rsed on this definition, we propose a clustering method which reeognises verbal 1)olysemics from a textual corpus. The results of experiments denmnst ra te the effectiveness of the proI)osed method. ...

Unsupervised word representations are very useful in NLP tasks both as inputs to learning algorithms and as extra word features in NLP systems. However, most of these models are built with only local context and one representation per word. This is problematic because words are often polysemous and global context can also provide useful information for learning word meanings. We present a new n...

In this paper Schapire and Singer’s AdaBoost.MH boosting algorithm is applied to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar–based approaches, which represent state–of–the–art accuracy on supervised WSD. In order to make boosting practical for a real learning domain o...

Although it is known that words acquire their meanings partly from the contexts in which they are used, we proposed that the way in which words are processed can also influence their representation. We further propose that individual differences in the way that words are processed can consequently lead to individual differences in the way that they are represented. Specifically, we showed that ...