Aligning Discourse and Argumentation Structures using Subtrees and Redescription Mining

International audienceIn this paper, we investigate similarities between discourse and argumentation structures by aligning subtrees in a corpus containing both annotations. Contrary to previous works, we focus on comparing sub-structures and not only relation matches. Using data mining techniques , we show that discourse and argumen-tation most often align well, and the double annotation allows to derive a mapping between structures. Moreover, this approach enables the study of similarities between discourse structures and differences in their expressive power

An Alignment Cost-Based Classification of Log Traces Using Machine-Learning

International audienceConformance checking is an important aspect of process mining that identifies the differences between the behaviors recorded in a log and those exhibited by an associated process model. Machine learning and deep learning methods perform extremely well in sequence analysis. We successfully apply both a Recurrent Neural Network and a Random Forest classifiers to the problem of evaluating whether the alignment cost of a log trace to a process model is below an arbitrary threshold, and provide a lower bound for the fitness of the process model based on the classification

AOC-Poset on discourse and argumentation subgraphs: what can we learn on their dependencies?

International audienceWe aim at finding and understanding dependencies between linguistic structures which differ in terms of constraints and expressive power. It has been shown that studying dependencies between the argu-mentation structure (ARG) and the Rhetorical Structure Theory (RST) is non-trivial and requires a fine methodology. In this paper, we propose to take advantage of the AOC-Poset structure to understand how the subgraphs alignements occur in a small corpus annotated in ARG and RST. We formalize the structures as graphs from which we extract both subgraphs and subgraphs alignments, matching those subgraphs which include the same text segments. Based on these extractions, we build a formal context where the objects are the texts and the attributes are the subgraphs and the subgraphs alignments. We show what we can learn from the dependencies between the structures by mining the AOC-Poset made of these attributes

Aligning Discourse and Argumentation Structures using Subtrees and Redescription Mining

International audienceIn this paper, we investigate similarities between discourse and argumentation structures by aligning subtrees in a corpus containing both annotations. Contrary to previous works, we focus on comparing sub-structures and not only relation matches. Using data mining techniques , we show that discourse and argumen-tation most often align well, and the double annotation allows to derive a mapping between structures. Moreover, this approach enables the study of similarities between discourse structures and differences in their expressive power

Alignement de Structures Argumentatives et Discursives par Fouille de Graphes et de Redescriptions

National audienceIn this paper, we investigate similarities between discourse and argumentation structures by aligning subtrees in a corpus containing both annotations. Contrary to previous works, we focus on comparing sub-structures and not only relation matches. Using data mining techniques , we show that discourse and argumen-tation most often align well, and the double annotation allows to derive a mapping between structures. Moreover, this approach enables the study of similarities between discourse structures and differences in their expressive power.Dans cet article, nous étudions la similarité entre structures argumen-tatives et discursives en alignant des sous-arbres dans un corpus annoté en RST et en structure argumentative. Contrairement aux travaux précédents, nous ne nous intéressons pas uniquement à un alignement relation à relation, mais à un alignement de sous-structures. À l'aide de méthodes de fouille de données, nous montrons que des similitudes existent entre l'argumentation et le discours. L'an-notation multiple du corpus permet également de proposer un alignement entre les structures. De plus, cette approche permet de mettre en évidence les diffé-rences d'expressivité des deux formalismes

Alignement de Structures Argumentatives et Discursives par Fouille de Graphes et de Redescriptions

National audienceIn this paper, we investigate similarities between discourse and argumentation structures by aligning subtrees in a corpus containing both annotations. Contrary to previous works, we focus on comparing sub-structures and not only relation matches. Using data mining techniques , we show that discourse and argumen-tation most often align well, and the double annotation allows to derive a mapping between structures. Moreover, this approach enables the study of similarities between discourse structures and differences in their expressive power.Dans cet article, nous étudions la similarité entre structures argumen-tatives et discursives en alignant des sous-arbres dans un corpus annoté en RST et en structure argumentative. Contrairement aux travaux précédents, nous ne nous intéressons pas uniquement à un alignement relation à relation, mais à un alignement de sous-structures. À l'aide de méthodes de fouille de données, nous montrons que des similitudes existent entre l'argumentation et le discours. L'an-notation multiple du corpus permet également de proposer un alignement entre les structures. De plus, cette approche permet de mettre en évidence les diffé-rences d'expressivité des deux formalismes

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

Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019

One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is that of a: "Public FAIR Knowledge Graph of Everything: We increasingly see the creation of knowledge graphs that capture information about the entirety of a class of entities. [...] This grand challenge extends this further by asking if we can create a knowledge graph of "everything" ranging from common sense concepts to location based entities. This knowledge graph should be "open to the public" in a FAIR manner democratizing this mass amount of knowledge." Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything. Surely, LOD provides a unique testbed for experimenting and evaluating research hypotheses on open and FAIR KG. One of the most neglected FAIR issues about KGs is their ongoing evolution and long term preservation. We want to investigate this problem, that is to understand what preserving and supporting the evolution of KGs means and how these problems can be addressed. Clearly, the problem can be approached from different perspectives and may require the development of different approaches, including new theories, ontologies, metrics, strategies, procedures, etc. This document reports a collaborative effort performed by 9 teams of students, each guided by a senior researcher as their mentor, attending the International Semantic Web Research School (ISWS 2019). Each team provides a different perspective to the problem of knowledge graph evolution substantiated by a set of research questions as the main subject of their investigation. In addition, they provide their working definition for KG preservation and evolution

Do Sentence Embeddings Capture Discourse Properties of Sentences from Scientific Abstracts ?

International audienceWe introduce four tasks designed to determine which sentence encoders best capture discourse properties of sentences from scientific abstracts, namely coherence between clauses of a sentence, and discourse relations within sentences. We show that even if contextual en-coders such as BERT or SciBERT encodes the coherence in discourse units, they do not help to predict three discourse relations commonly used in scientific abstracts. We discuss what these results underline, namely that these discourse relations are based on particular phrasing that allow non-contextual encoders to perform well