Prediction of Missing Semantic Relations in Lexical-Semantic Network using Random Forest Classifier

International audienceThis study focuses on the prediction of missing six semantic relations (such as is_a and has_part) between two given nodes in RezoJDM a French lexical-semantic network. The output of this prediction is a set of pairs in which the first entries are semantic relations and the second entries are the probabilities of existence of such relations. Due to the statement of the problem we choose the random forest (RF) predictor classifier approach to tackle this problem. We take for granted the existing semantic relations, for training/test dataset, gathered and validated by crowdsourcing. We describe how all of the mentioned ideas can be followed after using the node2vec approach in the feature extraction phase. We show how this approach can lead to acceptable results.Cette étude porte sur la prédiction de six relations sémantiques manquantes (telles que is_a et has_part) entre deux noeuds de RezoJDM, un réseau lexico-sémantique pour le français. Le résultat de cette prédiction est un ensemble de paires dans lesquelles les premières entrées sont des relations sémantiques et les secondes sont les probabilités d'existence de la relation. En raison de l'énoncé du problème, nous avons choisi d'utiliser un classifieur de forêt aléatoire pour le résoudre. Nous exploitons RezoJDM pour en extraire les relations sémantiques et construire nos données d'entraînement/test. Nous expliquons en quoi les idées développées peuvent être utilisées après l'utilisation de l'approche node2vec dans la phase de feature extraction. Nous montrons finalement comment cette approche conduit à des résultats prometteurs. Mots-clés. Apprentissage automatique, apprentissage supervisé, prédiction de relations sémantiques, classifieur de forêts aléatoires, réseau lexico-sémantique, traitement du langage naturel

Word Sense Distance and Similarity Patterns in Regular Polysemy - Insights Gained from Human Annotations of Graded Word Sense Similarity and an Investigation of Contextualised Language Models

This thesis investigates the notion of distance between different interpretations of polysemic words. It presents a novel, large-scale dataset containing a total of close to 18,000 human annotations rating both the nuanced sense similarity in lexically ambiguous word forms as well as the acceptability of combining their different sense interpretations in a single co-predication structure. The collected data suggests that different polysemic sense extensions can be perceived as significantly dissimilar in meaning, forming patterns of word sense similarity in some types of regular metonymic alternations. These observations question traditional theories postulating a fully under-specified mental representation of polysemic sense. Instead, the collected data supports more recent hypotheses of a structured representation of polysemy in the mental lexicon, suggesting some form of sense grouping, clustering, or hierarchical ordering based on word sense similarity. The new dataset then also is used to evaluate the performance of a range of contextualised language models in predicting graded word sense similarity. Our findings suggest that without any dedicated fine-tuning, especially BERT Large shows a relatively high correlation with the collected judgements. The model however struggles to consistently reproduce the similarity patterns observed in the human data, or to cluster word senses solely based on their contextualised embeddings. Finally, this thesis presents a pilot algorithm for automatically detecting words that exhibit a given polysemic sense alternation. Formulated in an unsupervised fashion, this algorithm is intended to bootstrap the collection of an even larger dataset of ambiguous language use that could be used in the fine-tuning or evaluation of computational language models for (graded) word sense disambiguation tasks

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Grounding event references in news

Events are frequently discussed in natural language, and their accurate identification is central to language understanding. Yet they are diverse and complex in ontology and reference; computational processing hence proves challenging. News provides a shared basis for communication by reporting events. We perform several studies into news event reference. One annotation study characterises each news report in terms of its update and topic events, but finds that topic is better consider through explicit references to background events. In this context, we propose the event linking task which—analogous to named entity linking or disambiguation—models the grounding of references to notable events. It defines the disambiguation of an event reference as a link to the archival article that first reports it. When two references are linked to the same article, they need not be references to the same event. Event linking hopes to provide an intuitive approximation to coreference, erring on the side of over-generation in contrast with the literature. The task is also distinguished in considering event references from multiple perspectives over time. We diagnostically evaluate the task by first linking references to past, newsworthy events in news and opinion pieces to an archive of the Sydney Morning Herald. The intensive annotation results in only a small corpus of 229 distinct links. However, we observe that a number of hyperlinks targeting online news correspond to event links. We thus acquire two large corpora of hyperlinks at very low cost. From these we learn weights for temporal and term overlap features in a retrieval system. These noisy data lead to significant performance gains over a bag-of-words baseline. While our initial system can accurately predict many event links, most will require deep linguistic processing for their disambiguation

Grounding event references in news

Events are frequently discussed in natural language, and their accurate identification is central to language understanding. Yet they are diverse and complex in ontology and reference; computational processing hence proves challenging. News provides a shared basis for communication by reporting events. We perform several studies into news event reference. One annotation study characterises each news report in terms of its update and topic events, but finds that topic is better consider through explicit references to background events. In this context, we propose the event linking task which—analogous to named entity linking or disambiguation—models the grounding of references to notable events. It defines the disambiguation of an event reference as a link to the archival article that first reports it. When two references are linked to the same article, they need not be references to the same event. Event linking hopes to provide an intuitive approximation to coreference, erring on the side of over-generation in contrast with the literature. The task is also distinguished in considering event references from multiple perspectives over time. We diagnostically evaluate the task by first linking references to past, newsworthy events in news and opinion pieces to an archive of the Sydney Morning Herald. The intensive annotation results in only a small corpus of 229 distinct links. However, we observe that a number of hyperlinks targeting online news correspond to event links. We thus acquire two large corpora of hyperlinks at very low cost. From these we learn weights for temporal and term overlap features in a retrieval system. These noisy data lead to significant performance gains over a bag-of-words baseline. While our initial system can accurately predict many event links, most will require deep linguistic processing for their disambiguation

Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition. Volume 2 : Traitement Automatique des Langues Naturelles

@ 6ème conférence conjointe: JEP-TALN-RECITAL 2020no abstrac