Frédérique Segond

Language technologies and patent search and classification

Abstract Research on a number of developments in language technologies, targeted at improving patent processing procedures within patent offices and in subsequent patent database search systems, is described. Aspects of patent processing covered are (1) OCR correction, to assist the conversion of paper documents to electronic versions, and (2) text classification, to assist in the allocation of new patent applications to the correct technical experts. Aspects of patent searching covered are (3) terminology enrichment, linking keywords to IPC terms, (4) table and figure reference extraction, to enable the corresponding text to be located, and (5) multilingual information access, to help formulate queries in other languages for the search of documents in those languages, and then to provide contextual translation of key sections of the documents retrieved. The authors conclude that these types of technologies will support more sophisticated search activities, for example in patent information centres, to complement the low-cost or free offerings on the Internet.

Error Driven Word Sense Disambiguation

In this paper we describe a method for performing word sense disambiguation (WSD). The method relies on unsupervised learning and exploits functional relations among words as produced by a shallow parser. By exploiting an error driven rule learning algorithm (Brill 1997), the system is able to produce rules for WSD, which can be optionally edited by humans in order to increase the performance of the system.

Formal description of multi-word lexemes with the finite-state formalism IDAREX

Most multi-word lexemes (MWLs) allow certain types of variation. This has to be taken into account for their description and their recognition in texts. We suggest to describe their syntactic restrictions and their idiosyncratic peculiarities with local grammar rules, which at the same time allow to express in a general way regularities valid for a whole class of MWLs. The local grammars can be written in a very convenient and compact way as regular expressions in the formalism IDAREX which uses a two-level morphology. IDAREX allows to define various types of variables, and to mix canonical and inflected word forms in the regular expressions.

Event Extraction for Legal Case Building and Reasoning

We are interested in developing tools to support the activities of lawyers in corporate litigation. In current applications, information such as characters that have played a significant role in a case, events in which they have participated, people they have been in contact, etc., have to be manually identified. There is little in the way of support to help them identify the relevant information in the first place. In this paper, we describe an approach to semi-automatically extracting such information from the collection of documents the lawyers are searching. Our approach is based on Natural Language Processing techniques and it enables the use of entity related information corresponding to the relations among the key players of a case, extracted in the form of events.

Addressing Risk Assessment for Patient Safety in Hospitals through Information Extraction in Medical Reports

Hospital Acquired Infections (HAI) is a real burden for doctors and risk surveillance experts. The impact on patients’ health and related healthcare cost is very significant and a major concern even for rich countries. Furthermore required data to evaluate the threat is generally not available to experts and that prevents from fast reaction. However, recent advances in Computational Intelligence Techniques such as Information Extraction, Risk Patterns Detection in documents and Decision Support Systems allow now to address this problem.

Dictionary-Driven Semantic Look-up

1. IntroductionThe French Semantic Dictionary Look-up (SDL) uses dictionary information aboutsubcategorization and collocates to perform Word Sense Disambiguation (WSD).The SDL is fully integrated in a multilingual comprehension system which uses theOxford Hachette French-English bilingual dictionary (OUP-H). Although the SDLworks on all words both for French and English, Romanseval results are relevantfor French verbs only because subcategorisation and collocate information is richerfor this part of speech in the OUP-H. The SDL uses dictionaries as semanticallytagged corpora of different languages, making the methodology reusable for anylanguage with existing on-line dictionaries.This paper first describes the system architecture as well as its components andresources. Second, it presents the work we did within Romanseval, namely sensemapping and results analysis.2. Semantic Dictionary Look-Up: Goal, Architecture and ComponentsThe SDL selects the most appropriate translation of a word appearing in a givencontext. It reorders dictionary entries making use of dictionary information. It isbuilt on top of Locolex,

TIER competency-based training course for the first receivers of CBRN casualties: a European perspective.

Introduction Education and training are key elements of health system preparedness vis-à-vis chemical, biological, radiological and nuclear (CBRN) emergencies. Medical respondents need sufficient knowledge and skills to manage the human impact of CBRN events. Objective The current study was designed to determine which competencies are needed by hospital staff when responding to CBRN emergencies, define educational needs to develop these competencies, and implement a suitable delivery method. Methods This study was carried out from September 2014 to February 2015, using a three-step modified Delphi method. On the basis of international experiences, publications, and experts’ consensus, core competencies for hospital staff – as CBRN casualty receivers – were determined, and training curricula and delivery methods were defined. Results The course consists of 10 domains. These are as follows: threat identification; health effects of CBRN agents; planning; hospital incident command system; information management; safety, personal protective equipment and decontamination; medical management; essential resources; psychological support; and ethical considerations. Expected competencies for each domain were defined. A blended approach was chosen. Conclusion By identifying a set of core competencies, this study aimed to provide the specific knowledge and skills required by medical staff to respond to CRBN emergencies. A blended approach may be a suitable delivery method, allowing medical staff to attend the same training sessions despite different time zones and locations. The study output provides a CBRN training scheme that may be adapted and used at the European Union level.

Évaluation d’un outil d’aide á l’anonymisation des documents médicaux basé sur le traitement automatique du langage naturel

Anonymization of personal data is a legal requirement for their use as part of a research project. In the context of developing a tool for detecting hospital-acquired infections, 2000 medical documents were needed for the research project ALADIN. To help annotators to anonymize this corpus of documents, a tool for the anonymization has been developed, relying on Natural Language Processing techniques. The recall, precision and F-score of the automatic phase of the anonymizer were respectively 79.7, 85.2 and 82.4%. The gold- standard used for the evaluation was the manual anonymization of the documents. The performance of the automatic anonymization can still be improved but the tool is already a considerable help in this process in terms of saving time and in terms of quality of anonymization (including the accuracy of labeling anonymized terms and computation of time duration).

Situational Language Training for Hotel Receptionists

This paper presents the lessons learned in experimenting with Thetis, an EC project focusing on the creation and localization of enhanced on-line pedagogical content for language learning in tourism industry. It is based on a general innovative approach to language learning that allows employees to acquire practical oral and written skills while navigating a relevant professional scenario. The approach is enabled by an underlying platform (EXILLS) that integrates virtual reality with a set of linguistic, technologies to create a new form of dynamic, extensible, goal-directed e-content.

Adapting Semantic Spreading Activation to Entity Linking in Text

The extraction and the disambiguation of knowledge guided by textual resources on the web is a crucial process to advance the Web of Linked Data. The goal of our work is to semantically enrich raw data by linking the mentions of named entities in the text to the corresponding known entities in knowledge bases. In our approach multiple aspects are considered: the prior knowledge of an entity in Wikipedia (i.e. the keyphraseness and commonness features that can be precomputed by crawling the Wikipedia dump), a set of features extracted from the input text and from the knowledge base, along with the correlation/relevancy among the resources in Linked Data. More precisely, this work explores the collective ranking approach formalized as a weighted graph model, in which the mentions in the input text and the candidate entities from knowledge bases are linked using the local compatibility and the global relatedness measures. Experiments on the datasets of the Open Knowledge Extraction (OKE) challenge with different configurations of our approach in each phase of the linking pipeline reveal its optimum mode. We investigate the notion of semantic relatedness between two entities represented as sets of neighbours in Linked Open Data that relies on an associative retrieval algorithm, with consideration of common neighbourhood. This measure improves the performance of prior link-based models and outperforms the explicit inter-link relevancy measure among entities (mostly Wikipedia-centric). Thus, our approach is resilient to non-existent or sparse links among related entities.