Monosemous Words Research Articles

The ambiguous nature of complex semantic types: an experimental investigation

Abstract Words with complex semantic types such as book are characterised by a multiplicity of interpretations that are not mutually exclusive (e.g., as a physical object and/or informational content). Their status with respect to lexical ambiguity is notoriously unclear, and it is debatable whether complex types are a particular form of polysemy (closely related to metonymy) or whether they belong to monosemy. In this study, we investigate the nature of complex types by conducting two experiments on ambiguous nouns in French. The first experiment collects speakers’ judgements about the sameness of meaning between different uses of complex-type, metonymic and monosemous words. The second experiment uses a priming paradigm and a sensicality task to investigate the online processing of complex-type words, as opposed to metonymic and monosemous words. Overall results indicate that, on a continuum of lexical ambiguity, complex types are closer to monosemy than to metonymy. The different interpretations of complex-type words are highly connected and fall under the same meaning, arguably in relation to a unique reference. These results suggest that complex types are associated with single underspecified entries in the mental lexicon. Moreover, they highlight the need for a model of lexical representations of ambiguous words that can account for the difference between complex types and metonymy.

Enhancing Accuracy of Semantic Relatedness Measurement by Word Single-Meaning Embeddings

We propose a lightweight algorithm of learning word single-meaning embeddings (WSME), by exploring WordNet synsets and Doc2vec document embeddings, to enhance the accuracy of semantic relatedness measurement. In our model, each polyseme is decomposed into a series of monosemous words with diverse WordNet synset tags which represent different word meanings, and there is a one-to-one correspondence between a word meaning and a vector. Our algorithm proceeds in 3 steps. First, the word sense disambiguation of each polyseme in different contexts is achieved by computing the maximum relatedness between the context of this polyseme and all its candidate meaning definitions in WordNet. Second, each tagged word is lemmatized according to its synset tag to alleviate the word sparsity problem caused by polysemes decomposition. Third, the word single-meaning embeddings are learned from the meaning-tagged corpus, and the semantic relatedness between words can be more accurately measured based on such embeddings. Our experimental results show that our algorithm achieves better performance on the semantic relatedness measurement compared with existing techniques.

Polysemie En Vertaling In Een Vreemde Taal

In this article we address the question whether polysemous words are more difficult to translate than monosemous words. If the two different meanings of a polysemous word have to be translated by two different words in the target language, the translator wil have to select the right one. We hypothesized that this choice would make the translation of polysemous words a more demanding task. In our investigation we operationalized the notion of difficulty in terms of response time: Does it take more time to translate polysemous words than to translate comparable monosemous words? We created an experimental setting permitting the measurement in milliseconds of the oral translation of words presented in small disambiguating contexts on a monitor. The experimental material consisted of polysemous words and the same number of monosemous words, which were comparable, by matching, in frequency and meaning. The results of our investigation induced us to adjust our hypothesis: Not all meanings of polysemous words seem to be more difficult to translate than monosemous words. The results showed that the more frequent meanings of the polysemous words were translated at about the same rate as their monosemous counterparts, whereas the translation of the less frequent ones took significantly more time than the translation of the comparable monosemous words. Probably, while translating a polysemous word, the translator goes straight to the translation of the most frequent meaning; when he sees that this translation doesn't fit in the given context, he continues his search for the translation that will fit better in the context.