Grouping Synonyms by Definitions

We present a method for grouping the synonyms of a lemma according to its dictionary senses. The senses are defined by a large machine readable dictionary for French, the TLFi (Tr\'esor de la langue fran\c{c}aise informatis\'e) and the synonyms are given by 5 synonym dictionaries (also for French). To evaluate the proposed method, we manually constructed a gold standard where for each (word, definition) pair and given the set of synonyms defined for that word by the 5 synonym dictionaries, 4 lexicographers specified the set of synonyms they judge adequate. While inter-annotator agreement ranges on that task from 67% to at best 88% depending on the annotator pair and on the synonym dictionary being considered, the automatic procedure we propose scores a precision of 67% and a recall of 71%. The proposed method is compared with related work namely, word sense disambiguation, synonym lexicon acquisition and WordNet construction

Automatic Identification of Aspectual Classes across Verbal Readings

International audienceThe automatic prediction of aspectual classes is very challenging for verbs whose aspectual value varies across readings, which are the rule rather than the exception. This paper sheds a new perspective on this problem by using a machine learning approach and a rich morpho-syntactic and semantic valency lexicon.In contrast to previous work, where the aspectual value of corpus clauses is determined on the basis of features retrieved from the corpus, we use features extracted from the lexicon, and aim to predict the aspectual value of verbal \textit{readings} rather than verbs.Studying the performance of the classifiers on a set of manually annotated verbal readings, we found that our lexicon provided enough information to reliably predict the aspectual value of verbs across their readings.We additionally tested our predictions for unseen predicates through a task based evaluation, by using them in the automatic detection of temporal relation types in TempEval 2007 tasks for French. These experiments also confirmed the reliability of our aspectual predictions, even for unseen verbs

Automated Semantic Classification of French Verbs

The aim of this work is to explore (semi-)automatic means to create a Levin-type classification of French verbs, suitable for Natural Language Processing. For English, a classification based on Levin's method is VerbNet (Kipper 2005). VerbNet is an extensive digital verb lexicon which systematically extends Levin's classes while ensuring that class members have a common semantics and share a common set of syntactic frames and thematic roles. In this work we reorganise the verbs from three French syntax lexicons, namely Volem, the Grammar-Lexicon (Ladl) and Dicovalence, into VerbNet-like verb classes using the technique of Formal Concept Analysis. We automatically acquire syntactic-semantic verb class and diathesis alternation information. We create large scale verb classes and compare their verb and frame distributions to those of VerbNet. We discuss possible evaluation schemes and finally focus on an evaluation methodology with respect to VerbNet, of which we present the theoretical motivation and analyse the feasibility on a small hand-built example

Représentation et Stockage des données de la numérisation du dictionnaire Trévoux

Le dictionnaire dit de « Trévoux » est un « dictionnaire universel françois et latin » dont lapremì eré edition á eté publié en 1704. Sa numérisation en vuè a la foi de la préservation et de la valorisation fait partie du programme du CPER ILD-ISTC. Dans le cadre de mes travaux pour READ j'ai exploré les outils et ressources de normali-sations mise en oeuvre dans d'autre projets de création de collections numériques, ce qui mêne à proposer un mode de stockage dans des formats XML standardisés en préservant les liens entre les images scannées, les objetsre connus parles OCR et des différentes versions du contenu textuel

Efficacy of sulfadoxine-pyrimethamine in Tanzania after two years as first-line drug for uncomplicated malaria: assessment protocol and implication for treatment policy strategies.

BACKGROUND\ud \ud Systematic surveillance for resistant malaria shows high level of resistance of Plasmodium falciparum to sulfadoxine-pyrimethamine (SP) across eastern and southern parts of Africa. This study assessed in vivo SP efficacy after two years of use as an interim first-line drug in Tanzania, and determined the rates of treatment failures obtained after 14 and 28 days of follow-up.\ud \ud METHODS\ud \ud The study was conducted in the Ipinda, Mlimba and Mkuranga health facilities in Tanzania. Children aged 6-59 months presenting with raised temperature associated exclusively with P. falciparum (1,000-100,000 parasites per microl) were treated with standard dose of SP. Treatment responses were classified according to the World Health Organization (WHO) definition as Adequate Clinical and Parasitological Response (ACPR), Early Treatment Failure (ETF), Late Clinical Failure (LCF) and Late Parasitological Failure (LPF) on day 14 and day 28.\ud \ud RESULTS\ud \ud Overall 196 (85.2%) of 230 patients had ACPR on day 14 but only 116 (50.9%) on day 28 (57.7% after excluding new infections by parasite genotyping). Altogether 21 (9.1%) and 13 (5.7%) of the 230 patients assessed up to day 14 and 39 (17.1%) and 55 (24.1%) of the 228 followed up to day 28 had clinical and parasitological failure, respectively.\ud \ud CONCLUSION\ud \ud These findings indicate that SP has low therapeutic value in Tanzania. The recommendation of changing first line treatment to artemether + lumefantrine combination therapy from early next year is, therefore, highly justified. These findings further stress that, for long half-life drugs such as SP, establishment of cut-off points for policy change in high transmission areas should consider both clinical and parasitological responses beyond day 14

Inviwo -- A Visualization System with Usage Abstraction Levels

The complexity of today's visualization applications demands specific visualization systems tailored for the development of these applications. Frequently, such systems utilize levels of abstraction to improve the application development process, for instance by providing a data flow network editor. Unfortunately, these abstractions result in several issues, which need to be circumvented through an abstraction-centered system design. Often, a high level of abstraction hides low level details, which makes it difficult to directly access the underlying computing platform, which would be important to achieve an optimal performance. Therefore, we propose a layer structure developed for modern and sustainable visualization systems allowing developers to interact with all contained abstraction levels. We refer to this interaction capabilities as usage abstraction levels, since we target application developers with various levels of experience. We formulate the requirements for such a system, derive the desired architecture, and present how the concepts have been exemplary realized within the Inviwo visualization system. Furthermore, we address several specific challenges that arise during the realization of such a layered architecture, such as communication between different computing platforms, performance centered encapsulation, as well as layer-independent development by supporting cross layer documentation and debugging capabilities

Bootstrapping a Classification of French Verbs Using Formal Concept Analysis.

International audienceWe use Formal Concept Analysis (FCA) to bootstrap a classification of French verbs. We show that the resulting classification has good factorisation power, compare it with the English Verbnet and report on a partial qualitative evaluation

Combining Formal Concept Analysis and Translation to Assign Frames and Thematic Role Sets to French Verbs

International audienceWe present an application of Formal Concept Analysis in the domain of Natural Language Processing: We give a general overview of the framework, describe its goals, the data it is based on, the way it works and we illustrate the kind of data we expect as a result. More specifically, we examine the ability of the stability, separation and probability indices to select the most relevant concepts with respect to our FCA application. We show that the sum of stability and separation gives results close to those obtained when using the entire lattice

Combining Formal Concept Analysis and Translation to Assign Frames and Semantic Role Sets to French Verbs

International audienceIn Natural Language Processing, verb classifications have been shown to be useful both theoretically (to capture syntactic and semantic generalisations about verbs) and practically (to support factorisation and the supervised learning of shallow semantic parsers). Acquiring such classifications manually is both costly and errror prone however. In this paper, we present a novel approach for automatically acquiring verb classifications. The approach uses FCA to build a concept lattice from existing linguistic resources; and stability and separation indices to extract from this lattice those concepts that most closely capture verb classes. The approach is evaluated on an established benchmark and shown to differ from previous approaches and in particular, from clustering approaches, in two main ways. First, it supports polysemy (because a verb may belong to several classes). Second, it naturally provides a syntactic and semantic characterisation of the verb classes produced (by creating concepts which systematically associate verbs with their syntactic and semantic attributes)