This paper describes our system, entitled IronyMagnet, for the 3rd Task of the SemEval 2018 workshop, “Irony Detection in English Tweets”. In Task 1, irony classification task has been considered as a binary classification task. Now for the first time, finer categories of irony are considered as part of a shared task. In task 2, three types of irony are considered; “Irony by contrast” - ironic instances where evaluative expression portrays inverse polarity (positive, negative) of the literal proposition; “Situational irony” - ironic instances where output of a situation do not comply with its expectation; “Other verbal irony” - instances where ironic intent does not rely on polarity contrast or unexpected outcome. We proposed a Siamese neural network for irony detection, which is consisted of two subnetworks, each containing a long short term memory layer(LSTM) and an embedding layer initialized with vectors from Glove word embedding 1 . The system achieved a f-score of 0.72, and 0.50 in task 1, and task 2 respectively.

Sarcasm is a pervasive phenomenon in social media, permitting the concise communication of meaning, affect and attitude. Concision requires wit to produce and wit to understand, which demands from each party knowledge of norms, context and a speaker’s mindset. Insight into a speaker’s psychological profile at the time of production is a valuable source of context for sarcasm detection. Using a neural architecture, we show significant gains in detection accuracy when knowledge of the speaker’s mood at the time of production can be inferred. Our focus is on sarcasm detection on Twitter, and show that the mood exhibited by a speaker over tweets leading up to a new post is as useful a cue for sarcasm as the topical context of the post itself. The work opens the door to an empirical exploration not just of sarcasm in text but of the sarcastic state of mind.

This paper describes our system, entitled Idiom Savant, for the 7th Task of the Semeval 2017 workshop, “Detection and interpretation of English Puns”. Our system consists of two probabilistic models for each type of puns using Google n-gram and Word2Vec. Our system achieved f-score of calculating, 0.663, and 0.07 in homographic puns and 0.8439, 0.6631, and 0.0806 in heterographic puns in task 1, task 2, and task 3 respectively.

Many of the beliefs that one uses to reason about everyday entities and events are neither strictly true or even logically consistent. Rather, people appear to rely on a large body of folk knowledge in the form of stereotypical associations, clichés and other kinds of naturalistic descriptions, many of which express views of the world that are second-hand, overly-simplified and, in some cases, non-literal to the point of being poetic. These descriptions pervade our language yet one rarely finds them in authoritative linguistic resources like dictionaries and encyclopaedias. We describe here how such naturalistic descriptions can be harvested from the web in the guise of explicit similes and related text patterns, and empirically demonstrate that these descriptions do broadly capture the way people see the world, at least from the perspective of category organization in an ontology.

Metonymy is a creative process that establishes relationships based on contiguity or semantic relatedness between concepts. We outline a mechanism for deriving new concepts from WordNet using metonymy. We argue that by exploiting polysemy in WordNet we can take advantage of the metonymic relations between concepts. The focus of our metonymy generation work has been the creation of noun­ noun compounds that do not already exist in WordNet and which can be profitably added to WordNet. The mechanism of metonymy generation we outline takes a source compound and creates new compounds by exploiting the polysemy associated with hyponyms of the head of the source compound. We argue that metonymy generation is a sound basis for concept creation as the newly created compounds are semantically related to the source concept. We demonstrate that metonymy generation based on polysemy is superior to a method of metonymy generation that ignores polysemy. These new concepts can be used to augment WordNet.