We consider the problem of distinguishing polysemous from homonymous nouns. This distinction is often taken for granted, but is seldom operationalized in the shape of an empirical model. We present a first step towards such a model, based on WordNet augmented with ontological classes provided by CoreLex. This model provides a polysemy index for each noun which (a), accurately distinguishes betw...

Our project is an extension of the project “Syntactico Semantic Word Representations in Multiple Languages”[1]. The previous project aims to improve the semantical representation of English vocabulary via incorporating the local context with global context and supplying homonymy and polysemy for multiple embeddings per word. It also introduces a new neural network architecture that learns the w...

Multifunctionality of a language generates multisemanticity of words. If different senses of word are systematically related, then how these senses are derived from each other and how they should be organised to reflect their regularity in sense denotation? Before this question is addressed, in this paper an attempt is made to identify salient traits of distinctions between the polysemous and t...

This paper discusses research on distinguishing word meanings in the context of information retrieval systems. We conducted experiments with three sources of evidence for making these distinctions: morphology, part-of-speech, and phrases. We have focused on the distinction between homonymy and polysemy (unrelated vs. related meanings). Our results support the need to distinguish homonymy and p ...

We examine a naming game with two agents trying to establish a common vocabulary for n objects. Such efforts lead to the emergence of language that allows for an efficient communication and exhibits some degree of homonymy and synonymy. Although homonymy reduces the communication efficiency, it seems to be a dynamical trap that persists for a long, and perhaps indefinite, time. On the other han...

We present an algorithm that acquires words (pairings of phonological forms and semantic representations) from larger utterances of unsegmented phoneme sequences and semantic representations. The algorithm maintains from utterance to utterance only a single coherent dictionary, and learns in the presence of homonymy, synonymy, and noise. Test results over a corpus of utterances generated from t...