Thierry Hamon

Improving term extraction with terminological resources

Studies of different term extractors on a corpus of the biomedical domain revealed decreasing performances when applied to highly technical texts. Facing the difficulty or impossibility to customize existing tools, we developed a tunable term extractor. It exploits linguistic-based rules in combination with the reuse of existing terminologies, i.e. exogenous disambiguation. Experiments reported here show that the combination of the two strategies allows the extraction of a greater number of term candidates with a higher level of reliability. We further describe the extraction process involving both endogenous and exogenous disambiguation implemented in the term extractor

A Step towards the Detection of Semantic Variants of Terms in Technical Documents

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Linguistic approach for identification of medication names and related information in clinical narratives

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Event-based information extraction for the biomedical domain: the Caderige project

This paper reports the results of a preliminary experiment on the detection of semantic variants of terms in a French technical document. The general goal of our work is to help the structuration of terminologies. Two kinds of semantic variants can be found in traditional terminologies: strict synonymy links and fuzzier relations like see-also. We have designed three rules which exploit general dictionary information to infer synonymy relations between complex candidate terms. The results have been examined by a human terminologist. The expert has judged that half of the overall pairs of terms are relevant for the semantic variation. He validated an important part of the detected links as synonymy. Moreover, it appeared that numerous errors are due to few mis-interpreted links: they could be eliminated by few exception rules.

Eventual situations for timeline extraction from clinical reports

BACKGROUND Pharmacotherapy is an integral part of any medical care process and plays an important role in the medical history of most patients. Information on medication is crucial for several tasks such as pharmacovigilance, medical decision or biomedical research. OBJECTIVES Within a narrative text, medication-related information can be buried within other non-relevant data. Specific methods, such as those provided by text mining, must be designed for accessing them, and this is the objective of this study. METHODS The authors designed a system for analyzing narrative clinical documents to extract from them medication occurrences and medication-related information. The system also attempts to deduce medications not covered by the dictionaries used. RESULTS Results provided by the system were evaluated within the framework of the I2B2 NLP challenge held in 2009. The system achieved an F-measure of 0.78 and ranked 7th out of 20 participating teams (the highest F-measure was 0.86). The system provided good results for the annotation and extraction of medication names, their frequency, dosage and mode of administration (F-measure over 0.81), while information on duration and reasons is poorly annotated and extracted (F-measure 0.36 and 0.29, respectively). The performance of the system was stable between the training and test sets.

Acquisition of elementary synonym relations from biological structured terminology

This paper gives an overview of the Caderige project. This project involves teams from different areas (biology, machine learning, natural language processing) in order to develop highlevel analysis tools for extracting structured information from biological bibliographical databases, especially Medline. The paper gives an overview of the approach and compares it to the state of the art.

Ontologies and terminologies: Continuum or dichotomy?

OBJECTIVE To identify the temporal relations between clinical events and temporal expressions in clinical reports, as defined in the i2b2/VA 2012 challenge. DESIGN To detect clinical events, we used rules and Conditional Random Fields. We built Random Forest models to identify event modality and polarity. To identify temporal expressions we built on the HeidelTime system. To detect temporal relations, we systematically studied their breakdown into distinct situations; we designed an oracle method to determine the most prominent situations and the most suitable associated classifiers, and combined their results. RESULTS We achieved F-measures of 0.8307 for event identification, based on rules, and 0.8385 for temporal expression identification. In the temporal relation task, we identified nine main situations in three groups, experimentally confirming shared intuitions: within-sentence relations, section-related time, and across-sentence relations. Logistic regression and Naïve Bayes performed best on the first and third groups, and decision trees on the second. We reached a 0.6231 global F-measure, improving by 7.5 points our official submission. CONCLUSIONS Carefully hand-crafted rules obtained good results for the detection of events and temporal expressions, while a combination of classifiers improved temporal link prediction. The characterization of the oracle recall of situations allowed us to point at directions where further work would be most useful for temporal relation detection: within-sentence relations and linking History of Present Illness events to the admission date. We suggest that the systematic situation breakdown proposed in this paper could also help improve other systems addressing this task.

Automatic Acquisition of Synonym Resources and Assessment of their Impact on the Enhanced Search in EHRs

Acquisition and enrichment of lexical resources have long been acknowledged as an important research in the area of computational linguistics. Nevertheless, we notice that such resources, particularly in specialised domains, are missing. However, specialised domains, i.e. biomedicine, propose several structured terminologies. In this paper, we propose a high-quality method for exploiting a structured terminology and inferring a specialised elementary synonym lexicon. The method is based on the analysis of syntactic structure of complex terms. We evaluate the approach on the biomedical domain by using the terminological resource Gene Ontology. It provides results with over 93% precision. Comparison with an existing synonym resource (the general-language resource WordNet) shows that there is a very small overlap between the induced lexicon of synonyms and the WordNet synsets.

Combining an Expert-Based Medical Entity Recognizer to a Machine-Learning System: Methods and a Case Study

Since there is a great confusion between the ontologies and other semantic resources, the purpose of this special issue is to address the question on “Ontologies and terminologies: Continuum or dichotomy”. We have selected five articles which study the differences and similarities between these semantic resources. More particularly, the articles are dedicated to the differences existing at the level of terms and of relations, the use of the ontologies on corpora and the dynamic and static representation of the knowledge.

Tuning HeidelTime for identifying time expressions in clinical texts in English and French

OBJECTIVE Currently, the use of natural language processing (NLP) approaches in order to improve search and exploration of electronic health records (EHRs) within healthcare information systems is not a common practice. One reason for this is the lack of suitable lexical resources. Indeed, in order to support such tasks, various types of such resources need to be collected or acquired (i.e., morphological, orthographic, synonymous). METHODS We propose a novel method for the acquisition of synonymy resources. This method is language-independent and relies on existence of structured terminologies. It enables to decipher hidden synonymy relations between simple words and terms on the basis of their syntactic analysis and exploitation of their compositionality. RESULTS Applied to series of synonym terms from the French subset of the UMLS , the method shows 99% precision. The overlap between thus inferred terms and the existing sparse resources of synonyms is very low. In order to better integrate these resources in an EHR search system, we analyzed a sample of clinical queries submitted by healthcare professionals. CONCLUSIONS Observation of clinical queries shows that they make a very little use of the query expansion function, and, whenever they do, synonymy relations are rarely involved.