Several NLP studies address the problem of figurative language, but among non-literal phenomena, they have neglected exaggeration.This paper presents a first computational approach to this figure of speech.We explore the possibility to automatically detect exaggerated sentences.First, we introduce HYPO, a corpus containing overstatements (or hyperboles) collected on the web and validated via crowdsourcing.Then, we evaluate a number of models trained on HYPO, and bring evidence that the task of hyperbole identification can be successfully performed based on a small set of semantic features.

Altering the lyrics of famous songs is a common creative and communicative act, often used for purposes that go beyond simple amusement, such as the creation of companion music for advertisements. In this case, the altered song commonly refers to the advertised product or idea. Here we present a system that can automatically reproduce this process: it starts from a novel text, i.e. the daily news, identifies the key concepts therein contained, expands them and then uses this word cloud to replace some of the words in a song, obeying lexical, metrical and rhyming constraints. The new song is then created by merging these new lyrics, sung by a speech synthesizer, with the original backing music. Our evaluation shows that songs created by the system increase the recall of the news they were created from.

In this paper, we experiment with a resource consisting of metaphorically annotated proverbs on the task of word-level metaphor recognition.We observe that existing feature sets do not perform well on this data.We design a novel set of features to better capture the peculiar nature of proverbs and we demonstrate that these new features are significantly more effective on the metaphorically dense proverb data.

While the effect of various lexical, syntactic, semantic and stylistic features have been addressed in persuasive language from a computational point of view, the persuasive effect of phonetics has received little attention.By modeling a notion of euphony and analyzing four datasets comprising persuasive and nonpersuasive sentences in different domains (political speeches, movie quotes, slogans and tweets), we explore the impact of sounds on different forms of persuasiveness.We conduct a series of analyses and prediction experiments within and across datasets.Our results highlight the positive role of phonetic devices on persuasion.

Connecting words with senses, namely, sight, hearing, taste, smell and touch, to comprehend the sensorial information in language is a straightforward task for humans by using commonsense knowledge. With this in mind, a lexicon associating words with senses would be crucial for the computational tasks aiming at interpretation of language. However, to the best of our knowledge, there is no systematic attempt in the literature to build such a resource. In this paper, we present a sensorial lexicon that associates English words with senses. To obtain this resource, we apply a computational method based on bootstrapping and corpus statistics. The quality of the resulting lexicon is evaluated with a gold standard created via crowdsourcing. The results show that a simple classifier relying on the lexicon outperforms two baselines on a sensory classification task, both at word and sentence level, and confirm the soundness of the proposed approach for the construction of the lexicon and the usefulness of the resource for computational applications.

Vocabulary acquisition constitutes a crucial but difficult and time consuming step of learning a foreign language. There exist several teaching methods which aim to facilitate this step by providing learners with various verbal and visual tips. However, building systems based on these methods is generally very costly since it requires so many resources such as time, money and human labor. In this paper, we introduce a fully automatized vocabulary teaching system which uses state-of-the-art natural language processing (NLP) and information retrieval (IR) techniques. For each foreign word the user is willing to learn, the system is capable of automatically producing memorization tips including key- words, sentences, colors, textual animations and images.

Most traditional recommender systems lack accuracy in the case where data used in the recommendation process is sparse. This study addresses the sparsity problem and aims to get rid of it by means of a content-boosted collaborative filtering approach applied to a web-based movie recommendation system. The main motivation is to investigate whether further success can be obtained by combining ‘local and global user similarity’ and ‘effective missing data prediction’ approaches, which were previously introduced and proved to be successful separately. The present work improves these approaches by taking the content information of the movies into account during the item similarity calculations. The comparison of the proposed approach with the original methods was carried out using mean absolute error, and more accurate predictions were achieved.