Tony Veale

A multidimensional approach for detecting irony in Twitter

Irony is a pervasive aspect of many online texts, one made all the more difficult by the absence of face-to-face contact and vocal intonation. As our media increasingly become more social, the problem of irony detection will become even more pressing. We describe here a set of textual features for recognizing irony at a linguistic level, especially in short texts created via social media such as Twitter postings or “tweets”. Our experiments concern four freely available data sets that were retrieved from Twitter using content words (e.g. “Toyota”) and user-generated tags (e.g. “#irony”). We construct a new model of irony detection that is assessed along two dimensions: representativeness and relevance. Initial results are largely positive, and provide valuable insights into the figurative issues facing tasks such as sentiment analysis, assessment of online reputations, or decision making.

Incongruity in humor: Root cause or epiphenomenon?

Abstract Humor and incongruity appear to be constant bedfellows, for at the heart of every joke one can point to some degree of absurdity, illogicality, or violation of expectation. This observation has lead many theories of humor to base themselves around some notion of incongruity or opposition, most notably the semantic-script theory (or SSTH) of Raskin and the subsequent general theory (or GTVH) of Attardo and Raskin. But correlation does not imply causality (a reality used to good effect in many successful examples of humor), and one should question whether incongruity serves a causal role in the workings and appreciation of humor or merely an epiphenomenal one. It remains a key question for humor researchers as to whether listeners react to incongruities by constructing humorous interpretations, or whether they collaboratively create these incongruities as a result of opportunistically constructing humorous interpretations.

Converging on the Divergent: The History (and Future) of the International Joint Workshops in Computational Creativity

We survey the history of studies of Computational Creativity, following the development of the International Conference on Computational Creativity from its beginnings, a decade ago, in two parallel workshop series. We give a brief outline of key issues, and a summary of the various different approaches taken by participants in the research field. The outlook is optimistic: a lot has been achieved in 10 years.

SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter

This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analysis of figurative language on Twitter (Task 11). This is the first sentiment analysis task wholly dedicated to analyzing figurative language on Twitter. Specifically, three broad classes of figurative language are considered: irony, sarcasm and metaphor. Gold standard sets of 8000 training tweets and 4000 test tweets were annotated using workers on the crowdsourcing platform CrowdFlower. Participating systems were required to provide a fine-grained sentiment score on an 11-point scale (-5 to +5, including 0 for neutral intent) for each tweet, and systems were evaluated against the gold standard using both a Cosinesimilarity and a Mean-Squared-Error measure.

The Challenges of Cross-Modal Translation: English-to-Sign-Language Translation in the Zardoz System

The sign languages used by deaf communities around the world represent a linguistic challenge that natural-language researchers in AI have only recently begun to take up. This challenge is particularly relevant to research in Machine Translation (MT), as natural sign languages have evolved in deaf communities into efficient modes of gestural communication, which differ from English not only in modality but in grammatical structure, exploiting a higher dimensionality of spatial expression. In this paper we describe Zardoz, an on-going AI research system that tackles the cross-modal MT problem, translating English text into fluid sign language. The paper presents an architectural overview of Zardoz, describing its central blackboard organization, the nature of its interlingual representation, and the major components which interact through this blackboard both to analyze the verbal input and generate the corresponding gestural output in one of a number of sign variants.

An Ironic Fist in a Velvet Glove: Creative Mis-Representation in the Construction of Ironic Similes

Irony is an effective but challenging mode of communication that allows a speaker to express viewpoints rich in sentiment with concision, sharpness and humour. Creative irony is especially common in online documents that express subjective and deeply-felt opinions, and thus represents a significant obstacle to the accurate analysis of sentiment in web texts. In this paper we look at one commonly used framing device for linguistic irony—the simile—to show how even the most creative uses of irony are often marked in ways that make them computationally feasible to detect. We conduct a very large corpus analysis of web-harvested similes to identify the most interesting characteristics of ironic comparisons, and provide an empirical evaluation of a new algorithm for separating ironic from non-ironic similes.

SemEval-2010 Task 9: The Interpretation of Noun Compounds Using Paraphrasing Verbs and Prepositions

We present a brief overview of the main challenges in understanding the semantics of noun compounds and consider some known methods. We introduce a new task to be part of SemEval-2010: the interpretation of noun compounds using paraphrasing verbs and prepositions. The task is meant to provide a standard testbed for future research on noun compound semantics. It should also promote paraphrase-based approaches to the problem, which can benefit many NLP applications.

A Fluid Knowledge Representation for Understanding and Generating Creative Metaphors

Creative metaphor is a phenomenon that stretches and bends the conventions of semantic description, often to humorous and poetic extremes. The computational modeling of metaphor thus requires a knowledge representation that is just as stretchable and semantically accommodating. We present here a flexible knowledge representation for metaphor interpretation and generation, called Talking Points, and describe how talking points can be acquired on a large scale from WordNet (Fellbaum, 1998) and from the web. We show how talking points can be fluidly connected to form a slipnet, and demonstrate that talking points provide an especially concise representation for concepts in general.

CONCEPTUAL SCAFFOLDING: A SPATIALLY FOUNDED MEANING REPRESENTATION FOR METAPHOR COMPREHENSION

Once viewed as a rhetorical and superficial language phenomenon, metaphor is now recognized to serve a fundamental role in our conceptual structuring and language comprehension processes. In particular, it is argued that certain experiential metaphors based upon intuitions of spatial relations are inherent in the conceptual organization of our most abstract thoughts. In this paper we present a two‐stage computational model of metaphor interpretation which employs a spatially founded semantics to broadly characterize the meaning carried by a metaphor in terms of a conceptual scaffolding, an interim meaning structure around which a fuller interpretation is fleshed out over time. We then present a semantics for the construction of conceptual scaffolding which is based upon core metaphors of collocation, containment and orientation. The goal of this scaffolding is to maintain the intended association of ideas even in contexts in which system knowledge is insufficient for a complete interpretation. This two‐stage system of scaffolding and elaboration also models the common time lapse between initial metaphor comprehension and full metaphor appreciation. Several mechanisms for deriving elaborative inference from scaffolding structures, particularly in cases of novel or creative metaphor, are also presented. While the system developed in this paper has significant practical application, it also demonstrates that core spatial metaphors clearly play a central role in metaphor comprehension.

A Concept-Centered Approach to Noun-Compound Interpretation

A noun-compound is a compressed proposition that requires an audience to recover the implicit relationship between two concepts that are expressed as nouns. Listeners recover this relationship by considering the most typical relations afforded by each concept. These relational possibilities are evident at a linguistic level in the syntagmatic patterns that connect nouns to the verbal actions that act upon, or are facilitated by, these nouns. We present a model of noun-compound interpretation that first learns the relational possibilities for individual nouns from corpora, and which then uses these to hypothesize about the most likely relationship that underpins a noun compound.