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https://ceur-ws.org/Vol-2244/paper_00.pdf
Introduction to the Second Workshop on
Natural Language for Artificial Intelligence
Pierpaolo Basile1 , Valerio Basile2 , Danilo Croce3
Felice DellOrletta4 , Marco Guerini5
1
University of Bari Aldo Moro, Italy
pierpaolo.basile@uniba.it
2
University of Turin, Italy
basile@di.unito.it
3
University of Roma, Tor Vergata, Italy
croce@info.uniroma2.it
4
Istituto di Linguistica Computazionale, Pisa, Italy
felice.dellorletta@ilc.cnr.it
5
Fondazione Bruno Kessler, Trento, Italy
guerini@fbk.eu
Natural Language Processing plays a fundamental role in current AI research,
as target of different scientific and industrial interests. At the same time, several
AI achievements have shown their beneficial impact on applications in linguis-
tic modelling, processing and generation. Especially the recent advancements
in deep learning are drastically changing the landscape of NLP, where the con-
tinuous performance improvement on well established tasks is happening at an
unprecedented speed. Therefore, Natural Language Processing is — still and
once again — a rich research topic, whose cross-fertilization with AI spans a
number of independent areas such as Cognitive Computing, Robotics as well
as Human-Computer Interaction. For AI, Natural Languages are the research
focus of paradigms and applications but, at the same time, they act as corner-
stones of automation, autonomy and learnability for most intelligent tasks. Such
tasks range from Computer Vision, to Planning and Social Behavior analysis,
up to more imponderable cognitive phenomena such as creativity and human
emotions. A reflection about such diverse and promising interactions is an im-
portant target for current AI studies, fully in the core mission of AI*IA. Still,
we also believe this area is not only “populated” of scientific and technological
challenges. In fact, we trust that at the crossroad between NLP and AI, new
technological paradigms rise: the resulting methodologies and technologies can
change our reality and their societal impact has not yet been fully-fledged.
Given these premises, the goal of the workshop “Natural Language for Ar-
tificial Intelligence” (NL4AI) is to provide a meeting forum for stimulating and
disseminating research where researchers (especially those affiliated with Italian
institutions) can network and discuss their results in an informal way6 . NL4AI-
2018 was the 2nd edition of this workshop, taking place on November 22nd and
23rd in Trento, Italy. We thank the Italian Association of Computational Linguis-
6
http://sag.art.uniroma2.it/NL4AI
1
tics (AILC)7 , that supported the invitation of three speakers to the workshop:
Malvina Nissim, Giuseppe Attardi and Oliviero Stock.
The fourteen contributions to the workshop covered several of the aforemen-
tioned topics, even more than one at a time, showing the interdependencies
among them. Here below we briefly review the contributions in light of such
topics.
One of the first areas of interest that clearly emerged was related to human
emotions, and in particular to Affective Computing with 5 papers devoted
to the topic. Coman et al. focused on the use of Neural Networks for Emoji
Prediction in Twitter, where the task is to predict the most salient Emoji to
be associated to a text. Alqarafi et al. also focused on sentiment in Twitter, by
describing a semi-Supervised methodology to build a corpus of annotated tweets
in Saudi. De Mattei et al., instead, focus on the relation between sentiment and
irony in a Multi-Task Learning framework. Villaneau et al. focus on detection
of aspects based sentiment analysis in French language, for the domain of Book
Reviews where the aspects are less easy to characterize. Finally, Leggeri et al.,
focus on the use of sentiment cues in Chatbot scenarios.
Other works are devoted to knowledge extraction from texts, in order to
enable complex inference tasks. Two works specifically focus on Named Entity
Recognition: Chen et al. focus on Transfer Learning for Named Entity Recogni-
tion in industrial settings, while Lauriola et al. focus on the Biomedical domain.
Some other work instead focused on representing several kinds of ontological
information, and how to structure knowledge that can be extracted from a text.
Gritz focuses on a new technique for improving lexicon-to-ontology mapping.
Mondal focuses on the task of inferring Semantic Networks and meaning relations
inside a lexicon. Basile et al. focus on mapping natural language terms to a Web
knowledge base. They show that incorporating NLP elements such as terms’
context and multi-word expressions treatment boost the performance. Finally,
Braun et al. address the problem of automatically extracting relations between
entities from online news and blog articles by using dependency parsing.
Finally, a set of works is focused on various Natural language processing
tasks and complex inference tasks, ranging from Question Answering to sentence
simplification. Gravina et al. focus on Answer Sentence Selection (ASS), one
of the steps typically involved in Question Answering, using a Cross-Attentive
Convolutional Neural Network. Schicchi et al., instead focuses on a neural model
for inducing the rules that identify the complexity of an Italian sentence, which
is needed for deciding whether a sentence needs of simplification. Croce et al.
focuses on a key topic in recent NLP approaches: the use of embeddings. In
particular, the author focus on how to efficiently combine kernel methods and
neural networks, to obtain unsupervised embeddings at the sentence level.
7
http://www.ai-lc.it/
2
As a final remark, the program co-chairs would like to thank all the members
of the Program Committee (see below) as well as the local organizers of the
AI*IA 2018 Conference8 .
– Giuseppe Attardi, University of Pisa
– Agnese Augello,ICAR-CNR
– Roberto Basili, University of Roma Tor Vergata
– Cristina Bosco, University of Turin
– Elena Cabrio, Universit Cte dAzur, CNRS, Inria, I3S, France
– Andrea Cimino, Istituto di Linguistica Computazionale Antonio Zampolli
(ILC-CNR)
– Berardina Nadja De Carolis,University of Bari
– Mauro Dragoni, Fondazione Bruno Kessler
– Alessandro Lenci, University of Pisa
– Alessandro Mazzei, University of Turin
– Alessandro Moschitti, Qatar Computing Research Institute
– Daniele Nardi, Sapienza University of Rome
– Malvina Nissim, University of Groningen
– Nicole Novielli, University of Bari
– Viviana Patti, University of Turin
– Giovanni Pilato, ICAR-CNR
– Elisa Ricci, University of Perugia
– Paolo Rosso, Universitat Politcnica de Valncia
– Giovanni Semeraro, University of Bari
– Rachele Sprugnoli, Fondazione Bruno Kessler / University of Trento
8
http://aixia2018.fbk.eu/index.php/home/
3