This paper proposes a simple test for compositionality (i.e., literal usage) of a word or phrase in a context-specific way. The test is computationally simple, relying on no external resources and only uses a set of trained word vectors. Experiments show that the proposed method is competitive with state of the art and displays high accuracy in context-specific compositionality detection of a variety of natural language phenomena (idiomaticity, sarcasm, metaphor) for different datasets in multiple languages. The key insight is to connect compositionality to a curious geometric property of word embeddings, which is of independent interest.
Sarcasm Detection Sarcasm is a figurative expression conveying a meaning that is opposite of its literal one, usually in an implicit way, and is a crucial component in sentiment analysis . Such connections are explored in @cite_4 via a rule-based method of identifying known sarcastic phrases. Semi-supervised sarcasm identification algorithms are identified in @cite_17 @cite_11 @cite_8 @cite_4 , each using different sets of features (eg., word senses, uni, bi and trigrams) that are then fed into a classification system tuned on a large training dataset. Metaphor Detection Metaphors offer figurative interpretations and are a key feature of natural language @cite_36 . @cite_37 considers metaphor expression as a mapping from a source domain to a target domain, and develops a corpus-based system, CorMet, to discover such metaphorical equivalances based on WordNet. @cite_31 hypothesises that metaphorical usage is related to the degreee of contextual abstractness, which they quantify relying on the MRC Psycholinguistic Database Machine Usable Dictionary (MRCPD) @cite_7 .
@cite_37 @cite_4 @cite_7 @cite_8 @cite_36 @cite_17 @cite_31 @cite_11
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Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an "apparently positive" sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network's baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase. The literature in automated sarcasm detection has mainly focused on lexical, syntactic and semantic-level analysis of text. However, a sarcastic sentence can be expressed with contextual presumptions, background and commonsense knowledge. In this paper, we propose CASCADE (a ContextuAl SarCasm DEtector) that adopts a hybrid approach of both content and context-driven modeling for sarcasm detection in online social media discussions. For the latter, CASCADE aims at extracting contextual information from the discourse of a discussion thread. Also, since the sarcastic nature and form of expression can vary from person to person, CASCADE utilizes user embeddings that encode stylometric and personality features of the users. When used along with content-based feature extractors such as Convolutional Neural Networks (CNNs), we see a significant boost in the classification performance on a large Reddit corpus. While a fair amount of work has been done on automatically detecting emotion in human speech, there has been little research on sarcasm detection. Although sarcastic speech acts are inherently subjective, humans have relatively clear intuitions as to what constitutes sarcastic speech. In this paper, we present a system for automatic sarcasm detection. Using a new acted speech corpus that is annotated for sarcastic and sincere speech, we examine a number of features that are indicative of sarcasm. The first set of features looks at a baseline of basic acoustic features that have been found to be helpful in human sarcasm identification. We then present an effective way of modeling and applying prosodic contours to the task of automatic sarcasm detection. This approach applies sequential modeling to categorical representations of pitch and intensity contours obtained via k-means clustering. Using a SimpleLogistic (LogitBoost) classifier, we are able to predict sarcasm with 81.57 accuracy. This result suggests that certain pitch and intensity contours are predictive of sarcastic speech. Automatic detection of figurative language is a challenging task in computational linguistics. Recognising both literal and figurative meaning is not trivial for a machine and in some cases it is hard even for humans. For this reason novel and accurate systems able to recognise figurative languages are necessary. We present in this paper a novel computational model capable to detect sarcasm in the social network Twitter (a popular microblogging service which allows users to post short messages). Our model is easy to implement and, unlike previous systems, it does not include patterns of words as features. Our seven sets of lexical features aim to detect sarcasm by its inner structure (for example unexpectedness, intensity of the terms or imbalance between registers), abstracting from the use of specific terms.