This paper presents a new version of a football reports generation system called PASS. The original version generated Dutch text and relied on a limited hand-crafted knowledge base. We describe how, in a short amount of time, we extended PASS to produce English texts, exploiting machine translation and Wikidata as a large-scale source of multilingual knowledge.

In this paper, we describe a methodology to infer Bullish or Bearish sentiment towards companies/brands. More specifically, our approach leverages affective lexica and word embeddings in combination with convolutional neural networks to infer the sentiment of financial news headlines towards a target company. Such architecture was used and evaluated in the context of the SemEval 2017 challenge (task 5, subtask 2), in which it obtained the best performance.

Musical parody, i.e. the act of changing the lyrics of an existing and very well-known song, is a commonly used technique for creating catchy advertising tunes and for mocking people or events. Here we describe a system for automatically producing a musical parody, starting from a corpus of songs. The system can automatically identify characterizing words and concepts related to a novel text, which are taken from the daily news. These concepts are then used as seeds to appropriately replace part of the original lyrics of a song, using metrical, rhyming and lexical constraints. Finally, the parody can be sung with a singing speech synthesizer, with no intervention from the user.

We present a methodology for building lexical sets for argument slots of Italian verbs. We start from an inventory of semantically typed Italian verb frames and through a mapping to WordNet we automatically annotate the sets of fillers for the argument positions in a corpus of sentences. We evaluate both a baseline algorithm and a syntax driven algorithm and show that the latter performs significantly better in terms of precision.

In computation linguistics a combination of syntagmatic and paradigmatic features is often exploited. While the first aspects are typically managed by information present in large n-gram databases, domain and ontological aspects are more properly modeled by lexical ontologies such as WordNet and semantic similarity spaces. This interconnection is even stricter when we are dealing with creative language phenomena, such as metaphors, prototypical properties, puns generation, hyperbolae and other rhetorical phenomena. This paper describes a way to focus on and accomplish some of these tasks by exploiting NgramQuery, a generalized query language on Google N-gram database. The expressiveness of this query language is boosted by plugging semantic similarity acquired both from corpora (e.g. LSA) and from WordNet, also integrating operators for phonetics and sentiment analysis. The paper reports a number of examples of usage in some creative language tasks.