Self-sufficiency has grown in popularity over the past decades as a response to ongoing societal crises. Considering the immediacy and gravity of the challenges at hand, it raises considerable debate within the public sphere, notably on widely used media such as YouTube. We propose to study this phenomenon by performing a sociological analysis of the technical knowledge around self-sufficiency in YouTube comment sections. Such study requires large corpora of annotated data, which are difficult to produce and typically scarce, especially in languages other than English. To overcome the problem, we implement in this work two independent methods to augment a dataset of labeled French-language YouTube comments. A first method, AugArg, relies on the use of argumentative structure as a controversy marker in the original corpus, while the other method, AugLLM, proposes to prompt an LLM to generate controversial comments. The fine-tuning of a CamemBERT model is performed using both the original and augmentated data, enabling us to compare the efficiency of both methods in representing controversiality in the context of self-sufficiency. Our results indicate that utilizing argumentative structure in the corpus provides a more controversy-representative set of comments in comparison with LLM-oriented methods.

One of the objectives of the Hérelles project is to find new mechanisms to facilitate the labeling (or semantization) of clusters from time series of satellite images. To achieve this, a proposed solution is to associate textual elements of interest with satellite data. The first step in this process consists of an automatic extraction of the information in the form of rules from urban planning documents composed in the French language. To address this challenge, we propose a method which is based on the multi-label classification of textual segments. It includes a special format for representing segments, in which each segment has a title and a subtitle. In addition, we propose a cascade approach aiming to deal with hierarchy of class labels. Finally, we develop several text augmentation techniques for the texts in French, which are able to improve the prediction results. We demonstrate experimentally that the resulting framework correctly classifies each type of segment with more than 90% of accuracy.

In this abstract, two methods for integrating textual data and textual features into ingestion processing are summarized. The first method involves integrating all features, including textual features, into dedicated frameworks, such as by using machine learning techniques. In the second method, text and textual features, such as keywords, are used to explain results returned by heterogeneous data mining. In this context, it is necessary to link data (e.g., databases, images, etc.) and/or obtained results with textual data (e.g., documents and keywords).

The following topics are dealt with: learning (artificial intelligence); data analysis; social networking (online); pattern classification; regression analysis; data mining; Internet; neural nets; graph theory; trees (mathematics).

International audience

Biomedical ontologies play an important role for information extraction in the biomedical domain. We present a workflow for updating automatically biomedical ontologies, composed of four steps. We detail two contributions concerning the concept extraction and semantic linkage of extracted terminology.

Comprehensive terminology is essential for a community to describe, exchange, and retrieve data. In multiple domain, the explosion of text data produced has reached a level for which automatic terminology extraction and enrichment is mandatory. Automatic Term Extraction (or Recognition) methods use natural language processing to do so. Methods featuring linguistic and statistical aspects as often proposed in the literature, solve some problems related to term extraction as low frequency, complexity of the multi-word term extraction, human effort to validate candidate terms. In contrast, we present two new measures for extracting and ranking muli-word terms from domain-specific corpora, covering the all mentioned problems. In addition we demonstrate how the use of the Web to evaluate the significance of a multi-word term candidate, helps us to outperform precision results obtain on the biomedical GENIA corpus with previous reported measures such as C-value.

A great deal of research on information extraction from textual datasets has been performed in specific data contexts, such as movie reviews, commercial product evaluations, campaign speeches, etc. In this paper, we raise the question on how appropriate these methods are for documents related to land-use planning. The kind of information sought concerns the stakeholders, sentiments, geographic information, and everything else related to the territory. However, it is extremely challenging to link sentiments to the three dimensions that constitute geographic information (location, time, and theme). After highlighting the limitations of existing proposals and discussing issues related to textual data, we present a method called OPILAND (OPinion mIning from LAND-use planning documents) designed to semi-automatically mine opinions related to named-entities in specialized contexts. Experiments are conducted on a Thau lagoon dataset (France), and then applied on three datasets that are related to different areas in order to highlight the relevance and the broader applications of our proposal.