Raphael Troncy

Also published as: Raphaël Troncy


2021

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Zero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles
Thomas Schleider | Raphael Troncy
Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

The knowledge of the European silk textile production is a typical case for which the information collected is heterogeneous, spread across many museums and sparse since rarely complete. Knowledge Graphs for this cultural heritage domain, when being developed with appropriate ontologies and vocabularies, enable to integrate and reconcile this diverse information. However, many of these original museum records still have some metadata gaps. In this paper, we present a zero-shot learning approach that leverages the ConceptNet common sense knowledge graph to predict categorical metadata informing about the silk objects production. We compared the performance of our approach with traditional supervised deep learning-based methods that do require training data. We demonstrate promising and competitive performance for similar datasets and circumstances and the ability to predict sometimes more fine-grained information. Our results can be reproduced using the code and datasets published at https://github.com/silknow/ZSL-KG-silk.

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Apples to Apples: A Systematic Evaluation of Topic Models
Ismail Harrando | Pasquale Lisena | Raphael Troncy
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

From statistical to neural models, a wide variety of topic modelling algorithms have been proposed in the literature. However, because of the diversity of datasets and metrics, there have not been many efforts to systematically compare their performance on the same benchmarks and under the same conditions. In this paper, we present a selection of 9 topic modelling techniques from the state of the art reflecting a diversity of approaches to the task, an overview of the different metrics used to compare their performance, and the challenges of conducting such a comparison. We empirically evaluate the performance of these models on different settings reflecting a variety of real-life conditions in terms of dataset size, number of topics, and distribution of topics, following identical preprocessing and evaluation processes. Using both metrics that rely on the intrinsic characteristics of the dataset (different coherence metrics), as well as external knowledge (word embeddings and ground-truth topic labels), our experiments reveal several shortcomings regarding the common practices in topic models evaluation.

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Neural Re-rankers for Evidence Retrieval in the FEVEROUS Task
Mohammed Saeed | Giulio Alfarano | Khai Nguyen | Duc Pham | Raphael Troncy | Paolo Papotti
Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)

Computational fact-checking has gained a lot of traction in the machine learning and natural language processing communities. A plethora of solutions have been developed, but methods which leverage both structured and unstructured information to detect misinformation are of particular relevance. In this paper, we tackle the FEVEROUS (Fact Extraction and VERification Over Unstructured and Structured information) challenge which consists of an open source baseline system together with a benchmark dataset containing 87,026 verified claims. We extend this baseline model by improving the evidence retrieval module yielding the best evidence F1 score among the competitors in the challenge leaderboard while obtaining an overall FEVEROUS score of 0.20 (5th best ranked system).

2020

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TOMODAPI: A Topic Modeling API to Train, Use and Compare Topic Models
Pasquale Lisena | Ismail Harrando | Oussama Kandakji | Raphael Troncy
Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)

From LDA to neural models, different topic modeling approaches have been proposed in the literature. However, their suitability and performance is not easy to compare, particularly when the algorithms are being used in the wild on heterogeneous datasets. In this paper, we introduce ToModAPI (TOpic MOdeling API), a wrapper library to easily train, evaluate and infer using different topic modeling algorithms through a unified interface. The library is extensible and can be used in Python environments or through a Web API.

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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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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The MeMAD Submission to the WMT18 Multimodal Translation Task
Stig-Arne Grönroos | Benoit Huet | Mikko Kurimo | Jorma Laaksonen | Bernard Merialdo | Phu Pham | Mats Sjöberg | Umut Sulubacak | Jörg Tiedemann | Raphael Troncy | Raúl Vázquez
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the MeMAD project entry to the WMT Multimodal Machine Translation Shared Task. We propose adapting the Transformer neural machine translation (NMT) architecture to a multi-modal setting. In this paper, we also describe the preliminary experiments with text-only translation systems leading us up to this choice. We have the top scoring system for both English-to-German and English-to-French, according to the automatic metrics for flickr18. Our experiments show that the effect of the visual features in our system is small. Our largest gains come from the quality of the underlying text-only NMT system. We find that appropriate use of additional data is effective.

2017

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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.

2016

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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.

2014

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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.

2012

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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