Giuseppe Rizzo


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MTSI-BERT: A Session-aware Knowledge-based Conversational Agent
Matteo Antonio Senese | Giuseppe Rizzo | Mauro Dragoni | Maurizio Morisio
Proceedings of the Twelfth Language Resources and Evaluation Conference

In the last years, the state of the art of NLP research has made a huge step forward. Since the release of ELMo (Peters et al., 2018), a new race for the leading scoreboards of all the main linguistic tasks has begun. Several models have been published achieving promising results in all the major NLP applications, from question answering to text classification, passing through named entity recognition. These great research discoveries coincide with an increasing trend for voice-based technologies in the customer care market. One of the next biggest challenges in this scenario will be the handling of multi-turn conversations, a type of conversations that differs from single-turn by the presence of multiple related interactions. The proposed work is an attempt to exploit one of these new milestones to handle multi-turn conversations. MTSI-BERT is a BERT-based model achieving promising results in intent classification, knowledge base action prediction and end of dialogue session detection, to determine the right moment to fulfill the user request. The study about the realization of PuffBot, an intelligent chatbot to support and monitor people suffering from asthma, shows how this type of technique could be an important piece in the development of future chatbots.


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JeuxDeLiens: Word Embeddings and Path-Based Similarity for Entity Linking using the French JeuxDeMots Lexical Semantic Network
Julien Plu | Kevin Cousot | Mathieu Lafourcade | Raphaël Troncy | Giuseppe Rizzo
Actes de la Conférence TALN. Volume 1 - Articles longs, articles courts de TALN

Entity linking systems typically rely on encyclopedic knowledge bases such as DBpedia or Freebase. In this paper, we use, instead, a French lexical-semantic network named JeuxDeMots to jointly type and link entities. Our approach combines word embeddings and a path-based similarity resulting in encouraging results over a set of documents from the French Le Monde newspaper.

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Framing Named Entity Linking Error Types
Adrian Braşoveanu | Giuseppe Rizzo | Philipp Kuntschik | Albert Weichselbraun | Lyndon J.B. Nixon
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Sanaphor++: Combining Deep Neural Networks with Semantics for Coreference Resolution
Julien Plu | Roman Prokofyev | Alberto Tonon | Philippe Cudré-Mauroux | Djellel Eddine Difallah | Raphaël Troncy | Giuseppe Rizzo
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification
Raphaël Troncy | Enrico Palumbo | Efstratios Sygkounas | Giuseppe Rizzo
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

In this paper, we describe the participation of the SentiME++ system to the SemEval 2017 Task 4A “Sentiment Analysis in Twitter” that aims to classify whether English tweets are of positive, neutral or negative sentiment. SentiME++ is an ensemble approach to sentiment analysis that leverages stacked generalization to automatically combine the predictions of five state-of-the-art sentiment classifiers. SentiME++ achieved officially 61.30% F1-score, ranking 12th out of 38 participants.


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Context-enhanced Adaptive Entity Linking
Filip Ilievski | Giuseppe Rizzo | Marieke van Erp | Julien Plu | Raphaël Troncy
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

More and more knowledge bases are publicly available as linked data. Since these knowledge bases contain structured descriptions of real-world entities, they can be exploited by entity linking systems that anchor entity mentions from text to the most relevant resources describing those entities. In this paper, we investigate adaptation of the entity linking task using contextual knowledge. The key intuition is that entity linking can be customized depending on the textual content, as well as on the application that would make use of the extracted information. We present an adaptive approach that relies on contextual knowledge from text to enhance the performance of ADEL, a hybrid linguistic and graph-based entity linking system. We evaluate our approach on a domain-specific corpus consisting of annotated WikiNews articles.

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Evaluating Entity Linking: An Analysis of Current Benchmark Datasets and a Roadmap for Doing a Better Job
Marieke van Erp | Pablo Mendes | Heiko Paulheim | Filip Ilievski | Julien Plu | Giuseppe Rizzo | Joerg Waitelonis
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Entity linking has become a popular task in both natural language processing and semantic web communities. However, we find that the benchmark datasets for entity linking tasks do not accurately evaluate entity linking systems. In this paper, we aim to chart the strengths and weaknesses of current benchmark datasets and sketch a roadmap for the community to devise better benchmark datasets.


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Benchmarking the Extraction and Disambiguation of Named Entities on the Semantic Web
Giuseppe Rizzo | Marieke van Erp | Raphaël Troncy
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Named entity recognition and disambiguation are of primary importance for extracting information and for populating knowledge bases. Detecting and classifying named entities has traditionally been taken on by the natural language processing community, whilst linking of entities to external resources, such as those in DBpedia, has been tackled by the Semantic Web community. As these tasks are treated in different communities, there is as yet no oversight on the performance of these tasks combined. We present an approach that combines the state-of-the art from named entity recognition in the natural language processing domain and named entity linking from the semantic web community. We report on experiments and results to gain more insights into the strengths and limitations of current approaches on these tasks. Our approach relies on the numerous web extractors supported by the NERD framework, which we combine with a machine learning algorithm to optimize recognition and linking of named entities. We test our approach on four standard data sets that are composed of two diverse text types, namely newswire and microposts.


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NERD: A Framework for Unifying Named Entity Recognition and Disambiguation Extraction Tools
Giuseppe Rizzo | Raphaël Troncy
Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics