Abstract
This thesis examines the construction of knowledge graphs from texts, focusing primarily on information extraction from unstructured texts. The objective of this work is to explore various aspects of information extraction from specialized corpora in the legal domain.
To this end, we divide this study into two subtasks : terminology extraction and relation extraction.
Terminology extraction aims to automatically identify relevant terms in a given corpus of texts. Subsequently, from these terms, we extract the relations that link them. For relation extraction, two approaches are conceivable : either by determining in advance the types of relations that structure the terms, or by using the context, notably the verbs or other actions that link these terms (in the field of OpenIE). Thus, we address three main sub-problems.
We introduce the terminology extraction system InfoGlean KeyTerms, composed of three modules : one for named entity recognition (NER), one for the extraction of relevant terms/text segments (KPE), and a final one for the extraction of legal entities. Expert annotations were provided in addition to this system to constitute a terminological base.
After building this terminological base, we implemented two relation extraction systems : Relational Embeddings Model (REM) and GPT Open Relation EXtraction (GOREX). REM identifies typed relations between the extracted terms using the lexical network rezo-JDM. REM represents the relation pairs using a Word2Vec model, then classifies the types of relations. GOREX, on the other hand, exploits the principle of OpenIE by focusing on the verbs or action terms in the local context of the terms. GOREX uses LLMs to perform this task.
The analysis of the results revealed promising research avenues to be explored in future work for all systems. More specifically, the implementation of a hybrid relation extraction system could be an interesting path to explore.