Sarcasm is generally characterized as a figure of speech that involves the substitution of a literal by a figurative meaning, which is usually the opposite of the original literal meaning. We re-frame the sarcasm detection task as a type of word sense disambiguation problem, where the sense of a word is either literal or sarcastic. We call this the Literal/Sarcastic Sense Disambiguation (LSSD) ...

In this paper we describe experiments with automated detection of metaphors in the Polish language. We focus our analysis on noun phrases composed of an adjective and a noun, and distinguish three types of expressions: with literal sense, with metaphorical sense, and expressions both literal and methaphorical (context-dependent). We propose a method of automatically recognizing expression type ...

We study the algebraizability of the logics constructed using literal-paraconsistent and literal-paracomplete matrices described by Lewin and Mikenbergin[11] proving that theyare all algebraizable in the sense of Blok We study the algebraizability of the logics constructed using literal-paraconsistent and literal-paracomplete matrices described by Lewin and Mikenberg in [11], proving that they ...

Metonymic location names refer to other, related entities and possess a meaning different from the literal, geographic sense. Metonymic names are to be treated differently to improve performance of geographic information retrieval (GIR). This paper presents a method for disambiguating location names in textual information to distinguish literal and metonymic senses, based on shallow features. T...

Event-related brain potentials (ERPs) were recorded from 18 normal adults as they read sentences that ended with words used literally, metaphorically, or in an intermediate literal mapping condition. In the latter condition, the literal sense of the word was used in a way that prompted readers to map conceptual structure from a different domain. ERPs measured from 300 to 500 msec after the onse...

Decades of lexical ambiguity research has rigorously studied effects of relative sense frequency on sense disambiguation in biased contexts, while fundamental semantic issues such as distinction of different types of ambiguities, or influences from lexical meanings’ semantic nature (e.g., literal or metaphorical) as well as these meanings’ degrees of conventionalization, have received less atte...

Experimental studies on polysemy have come to contradictory conclusions on whether words with multiple senses are stored as separate or shared mental representations. The present study examined the semantic relatedness and semantic similarity of literal and non-literal (metonymic and metaphorical) senses of three word classes: nouns, verbs, and adjectives. Two methods were used: a psycholinguis...

In this thesis we present TroFi, a system for separating literal and nonliteral usages of verbs through unsupervised statistical word-sense disambiguation and clustering techniques. TroFi distinguishes itself by redefining the types of nonliteral language handled and by depending purely on sentential context rather than selectional constraint violations and paths in semantic hierarchies. TroFi ...

When computer programs disambiguate words in a sentence, they often encounter non-literal or novel usages not included in their lexicon. In a recent study, Georgia Green (personal communication) estimated that 17% to 20% of the content word senses encountered in various types of normal English text are not fisted in the dictionary. While these novel word senses are generally valid, they occur i...

In this paper we present TroFi (Trope Finder), a system for automatically classifying literal and nonliteral usages of verbs through nearly unsupervised word-sense disambiguation and clustering techniques. TroFi uses sentential context instead of selectional constraint violations or paths in semantic hierarchies. It also uses literal and nonliteral seed sets acquired and cleaned without human s...