Verb valency lexicon VerbaLex is one of few language resources which take a local context into account. For each Czech verb with its sense, VerbaLex contains appropriate valency frames (patterns with morphological and syntactical information) in which the verb can appear. This, and the fact that a context of a word is crucial for determining sense of the word, makes VerbaLex suitable for disamb...

Polysemous adjectives seem to make irregular contributions to the meaning of adjective-noun phrases. This thesis provides a systematic explanation of this problem. Polysemous adjectives are considered semantically underspecified. The underspecified semantic meaning has to be pragmatically enriched with due consideration of world-knowledge and linguistic context. The treatment follows Blutner’s ...

This paper presents a supervised machine learning approach that aims at annotating those homograph word forms in WordNet that share some common meaning and can hence be thought of as belonging to a polysemous word. Using different graph-based measures, a set of features is selected, and a random forest model is trained and evaluated. The results are compared to other features used for polysemy ...

This paper presents a set of tools and methods for acquiring, manipulating, and analyzing machine-readable dictionaries. We give several detailed examples of the use of these tools and methods for particular analyses. A novel aspect of our work is that it allows the combined processing of multiple machine-readable dictionaries. Our examples describe analyses of data from Webster's Seventh Colle...

Word Sense Disambiguation (WSD) is the task of choosing the right sense of a polysemous word given a context. It is obviously essential for many natural language processing applications such as human-computer communication, machine translation, and information retrieval. In recent years, much attention have been paid to improve the performance of WSD systems by using combination of classifiers....

Probabilistic Latent Semantic Indexing is a novel approach to automated document indexing which is based on a statistical latent class model for factor analysis of count data. Fitted from a training corpus of text documents by a generalization of the Expectation Maximization algorithm, the utilized model is able to deal with domain{speci c synonymy as well as with polysemous words. In contrast ...

Previous researches have shown that learning multiple representations for polysemous words can improve the performance of word embeddings on many tasks. However, this leads to another problem. Several vectors of a word may actually point to the same meaning, namely pseudo multi-sense. In this paper, we introduce the concept of pseudo multi-sense, and then propose an algorithm to detect such cas...

The wide use of abbreviations in modern texts poses interesting challenges and opportunities in the field of NLP. In addition to their dynamic nature, abbreviations are highly polysemous with respect to regular words. Technologies that exhibit some level of language understanding may be adversely impacted by the presence of abbreviations. This paper addresses two related problems: (1) expansion...

If concepts, categories, and word meanings are stable, how can people use them so flexibly? Here we explore a possible answer: maybe this stability is an illusion. Perhaps all concepts, categories, and word meanings (CC&Ms) are constructed ad hoc, each time we use them. On this proposal, all words are infinitely polysemous, all communication is ’good enough’, and no idea is ever the same twice....

It has been shown that learning distributed word representations is highly useful for Twitter sentiment classification. Most existing models rely on a single distributed representation for each word. This is problematic for sentiment classification because words are often polysemous and each word can contain different sentiment polarities under different topics. We address this issue by learnin...