Since the announcement of the GDPR, the pseudonymization of legal documents has become a high-priority task in many legal organizations. This means that for making public a document, it is necessary to redact the identity of certain entities, such as witnesses. In this work, we present the first results obtained by PSILENCE, a pseudonymization tool created for redacting semi-automatically international arbitration documents in English. PSILENCE has been built using a Named Entity Recognition (NER) system, along with a Coreference Resolution system. These systems allow us to find the people that we need to redact in a clustered way, but also to propose the same pseudonym throughout one document. This last aspect makes it easier to read and comprehend a redacted legal document. Different experiments were done on four different datasets, one of which was legal, and the results are promising, reaching a Macro F-score of up to 0.72 on the legal dataset.

In this work, we present a Named Entity Recognition (NER) system that was trained using a Frustratingly Easy Domain Adaptation (FEDA) over multiple legal corpora. The goal was to create a NER capable of detecting 14 types of legal named entities in Indian judgments. Besides the FEDA architecture, we explored a method based on overlapping context and averaging tensors to process long input texts, which can be beneficial when processing legal documents. The proposed NER reached an F1-score of 0.9007 in the sub-task B of Semeval-2023 Task 6, Understanding Legal Texts.

This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program. The collected resources were offered to participants of a hackathon organized as part of the EACL Hackashop on News Media Content Analysis and Automated Report Generation in February 2021. The hackathon had six participating teams who addressed different challenges, either from the list of proposed challenges or their own news-industry-related tasks. This paper goes beyond the scope of the hackathon, as it brings together in a coherent and compact form most of the resources developed, collected and released by the EMBEDDIA project. Moreover, it constitutes a handy source for news media industry and researchers in the fields of Natural Language Processing and Social Science.

In this study, we present an exploratory analysis of a Slovenian news corpus, in which we investigate the association between named entities and sentiment in the news. We propose a methodology that combines Named Entity Recognition and Subgroup Discovery - a descriptive rule learning technique for identifying groups of examples that share the same class label (sentiment) and pattern (features - Named Entities). The approach is used to induce the positive and negative sentiment class rules that reveal interesting patterns related to different Slovenian and international politicians, organizations, and locations.

We present a collection of Named Entity Recognition (NER) systems for six Slavic languages: Bulgarian, Czech, Polish, Slovenian, Russian and Ukrainian. These NER systems have been trained using different BERT models and a Frustratingly Easy Domain Adaptation (FEDA). FEDA allow us creating NER systems using multiple datasets without having to worry about whether the tagset (e.g. Location, Event, Miscellaneous, Time) in the source and target domains match, while increasing the amount of data available for training. Moreover, we boosted the prediction on named entities by marking uppercase words and predicting masked words. Participating in the 3rd Shared Task on SlavNER, our NER systems reached a strict match micro F-score of up to 0.908. The results demonstrate good generalization, even in named entities with weak regularity, such as book titles, or entities that were never seen during the training.

This paper tackles the task of named entity recognition (NER) applied to digitized historical texts obtained from processing digital images of newspapers using optical character recognition (OCR) techniques. We argue that the main challenge for this task is that the OCR process leads to misspellings and linguistic errors in the output text. Moreover, historical variations can be present in aged documents, which can impact the performance of the NER process. We conduct a comparative evaluation on two historical datasets in German and French against previous state-of-the-art models, and we propose a model based on a hierarchical stack of Transformers to approach the NER task for historical data. Our findings show that the proposed model clearly improves the results on both historical datasets, and does not degrade the results for modern datasets.

A scientific vocabulary is a set of terms that designate scientific concepts. This set of lexical units can be used in several applications ranging from the development of terminological dictionaries and machine translation systems to the development of lexical databases and beyond. Even though automatic term recognition systems exist since the 80s, this process is still mainly done by hand, since it generally yields more accurate results, although not in less time and at a higher cost. Some of the reasons for this are the fairly low precision and recall results obtained, the domain dependence of existing tools and the lack of available semantic knowledge needed to validate these results. In this paper we present a method that uses Wikipedia as a semantic knowledge resource, to validate term candidates from a set of scientific text books used in the last three years of high school for mathematics, health education and ecology. The proposed method may be applied to any domain or language (assuming there is a minimal coverage by Wikipedia).