Un système de détection d'entités nommées adapté pour la campagne d'évaluation ESTER 2

In this paper, we report our participation to the ESTER 2 (Evaluation des Systèmes de Transcription Enrichie d¿Emissions Radiophoniques) evaluation campaign. After describing the goals, specificities and challenges of the campaign, we present our named entity detection system and focus on the adaptations made in the framework of the campaign. We present the results obtained during the competition and then new results obtained afterward. We then conclude by the lessons we learned from this experiment.JRC.G.2-Global security and crisis managemen

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

Building Multilingual Named Entity Annotated Corpora Exploiting Parallel Corpora

Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora AEPC 2010. Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk. NEALT Proceedings Series, Vol. 10 (2010), 24-33. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15893

Vers une méthodologie d'annotation des entités nommées en corpus ?

National audienceToday, the named entity recognition task is considered as fundamental, but it involves some specific difficulties in terms of annotation. We list them here, with illustrations taken from manual annotation experiments in microbiology. Those issues lead us to ask the fun- damental question of what the annotators should annotate and, even more important, for which purpose. We thus identify the applications using named entity recognition and, according to the real needs of those applications, we propose to semantically define the elements to annotate. Finally, we put forward a number of methodological recommendations to ensure a coherent and reliable annotation scheme

JRC-Names: Multilingual Entity Name variants and titles as Linked Data

Since 2004 the European Commission’s Joint Research Centre (JRC) has been analysing the online version of printed media in over twenty languages and has automatically recognised and compiled large amounts of named entities (persons and organisations) and their many name variants. The collected variants not only include standard spellings in various countries, languages and scripts, but also frequently found spelling mistakes or lesser used name forms, all occurring in real-life text (e.g. Benjamin/Binyamin/Bibi/Benyamín/Biniamin/Беньямин/ بنیامین Netanyahu/ Netanjahu/Nétanyahou/Netahnyahu/Нетаньяху/ نتنیاهو ). This entity name variant data, known as JRCNames, has been available for public download since 2011. In this article, we report on our efforts to render JRC-Names as Linked Data (LD), using the lexicon model for ontologies lemon. Besides adhering to Semantic Web standards, this new release goes beyond the initial one in that it includes titles found next to the names, as well as date ranges when the titles and the name variants were found. It also establishes links towards existing datasets, such as DBpedia and Talk-Of-Europe. As multilingual linguistic linked dataset, JRC-Names can help bridge the gap between structured data and natural languages, thus supporting large-scale data integration, e.g. cross-lingual mapping, and web-based content processing, e.g. entity linking. JRC-Names is publicly available through the dataset catalogue of the European Union’s Open Data Portal.JRC.G.2-Global security and crisis managemen

Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplying with, as a first essential step, document layout analysis. If the identification and categorization of segments of interest in document images have seen significant progress over the last years thanks to deep learning techniques, many challenges remain with, among others, the use of finer-grained segmentation typologies and the consideration of complex, heterogeneous documents such as historical newspapers. Besides, most approaches consider visual features only, ignoring textual signal. In this context, we introduce a multimodal approach for the semantic segmentation of historical newspapers that combines visual and textual features. Based on a series of experiments on diachronic Swiss and Luxembourgish newspapers, we investigate, among others, the predictive power of visual and textual features and their capacity to generalize across time and sources. Results show consistent improvement of multimodal models in comparison to a strong visual baseline, as well as better robustness to high material variance

Acronym recognition and processing in 22 languages

We are presenting work on recognising acronyms of the form Long-Form (Short-Form) such as "International Monetary Fund (IMF)" in millions of news articles in twenty-two languages, as part of our more general effort to recognise entities and their variants in news text and to use them for the automatic analysis of the news, including the linking of related news across languages. We show how the acronym recognition patterns, initially developed for medical terms, needed to be adapted to the more general news domain and we present evaluation results. We describe our effort to automatically merge the numerous long-form variants referring to the same short-form, while keeping non-related long-forms separate. Finally, we provide extensive statistics on the frequency and the distribution of short-form/long-form pairs across languages

Named Entity Resources - Overview and Outlook

Recognition of real-world entities is crucial for most NLP applications. Since its introduction some twenty years ago, named entity processing has undergone a significant evolution with, among others, the definition of new tasks (e.g. entity linking) and the emergence of new types of data (e.g. speech transcriptions, micro-blogging). These pose certainly new challenges which affect not only methods and algorithms but especially linguistic resources. Where do we stand with respect to named entity resources? This paper aims at providing a systematic overview of named entity resources, accounting for qualities such as multilingualism, dynamicity and interoperability, and to identify shortfalls in order to guide future developments

Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplying with, as a first essential step, document layout analysis. If the identification and categorization of segments of interest in document images have seen significant progress over the last years thanks to deep learning techniques, many challenges remain with, among others, the use of finer-grained segmentation typologies and the consideration of complex, heterogeneous documents such as historical newspapers. Besides, most approaches consider visual features only, ignoring textual signal. In this context, we introduce a multimodal approach for the semantic segmentation of historical newspapers that combines visual and textual features. Based on a series of experiments on diachronic Swiss and Luxembourgish newspapers, we investigate, among others, the predictive power of visual and textual features and their capacity to generalize across time and sources. Results show consistent improvement of multimodal models in comparison to a strong visual baseline, as well as better robustness to high material variance

Extended Overview of HIPE-2022: Named Entity Recognition and Linking in Multilingual Historical Documents

This paper presents an overview of the second edition of HIPE (Identifying Historical People, Places and other Entities), a shared task on named entity recognition and linking in multilingual historical documents. Following the success of the first CLEF-HIPE-2020 evaluation lab, HIPE-2022 confronts systems with the challenges of dealing with more languages, learning domain-specific entities, and adapting to diverse annotation tag sets. This shared task is part of the ongoing efforts of the natural language processing and digital humanities communities to adapt and develop appropriate technologies to efficiently retrieve and explore information from historical texts. On such material, however, named entity processing techniques face the challenges of domain heterogeneity, input noisiness, dynamics of language, and lack of resources. In this context, the main objective of HIPE-2022, run as an evaluation lab of the CLEF 2022 conference, is to gain new insights into the transferability of named entity processing approaches across languages, time periods, document types, and annotation tag sets. Tasks, corpora, and results of participating teams are presented. Compared to the condensed overview [1], this paper contains more refined statistics on the datasets, a break down of the results per type of entity, and a discussion of the ‘challenges’ proposed in the shared task