Active document enrichment using adaptive information extraction from text

The traditional process of document annotation for knowledge identification and extraction in the Semantic Web (SW) is complex and time consuming, as it requires manual annotation by domain experts. There is currently a strong interest in Text Mining technologies (and in particular in Human Language-based Technologies), for reducing the burden of text annotation for Knowledge Management (KM). In this poster we present Melita, an annotation interface that uses Adaptive Information Extraction from texts for reducing the burden of text annotation.peer-reviewe

Information Extraction from Text for Improving Research on Small Molecules and Histone Modifications

The cumulative number of publications, in particular in the life sciences, requires efficient methods for the automated extraction of information and semantic information retrieval. The recognition and identification of information-carrying units in text – concept denominations and named entities – relevant to a certain domain is a fundamental step. The focus of this thesis lies on the recognition of chemical entities and the new biological named entity type histone modifications, which are both important in the field of drug discovery. As the emergence of new research fields as well as the discovery and generation of novel entities goes along with the coinage of new terms, the perpetual adaptation of respective named entity recognition approaches to new domains is an important step for information extraction. Two methodologies have been investigated in this concern: the state-of-the-art machine learning method, Conditional Random Fields (CRF), and an approximate string search method based on dictionaries. Recognition methods that rely on dictionaries are strongly dependent on the availability of entity terminology collections as well as on its quality. In the case of chemical entities the terminology is distributed over more than 7 publicly available data sources. The join of entries and accompanied terminology from selected resources enables the generation of a new dictionary comprising chemical named entities. Combined with the automatic processing of respective terminology – the dictionary curation – the recognition performance reached an F1 measure of 0.54. That is an improvement by 29 % in comparison to the raw dictionary. The highest recall was achieved for the class of TRIVIAL-names with 0.79. The recognition and identification of chemical named entities provides a prerequisite for the extraction of related pharmacological relevant information from literature data. Therefore, lexico-syntactic patterns were defined that support the automated extraction of hypernymic phrases comprising pharmacological function terminology related to chemical compounds. It was shown that 29-50 % of the automatically extracted terms can be proposed for novel functional annotation of chemical entities provided by the reference database DrugBank. Furthermore, they are a basis for building up concept hierarchies and ontologies or for extending existing ones. Successively, the pharmacological function and biological activity concepts obtained from text were included into a novel descriptor for chemical compounds. Its successful application for the prediction of pharmacological function of molecules and the extension of chemical classification schemes, such as the the Anatomical Therapeutic Chemical (ATC), is demonstrated. In contrast to chemical entities, no comprehensive terminology resource has been available for histone modifications. Thus, histone modification concept terminology was primary recognized in text via CRFs with a F1 measure of 0.86. Subsequent, linguistic variants of extracted histone modification terms were mapped to standard representations that were organized into a newly assembled histone modification hierarchy. The mapping was accomplished by a novel developed term mapping approach described in the thesis. The combination of term recognition and term variant resolution builds up a new procedure for the assembly of novel terminology collections. It supports the generation of a term list that is applicable in dictionary-based methods. For the recognition of histone modification in text it could be shown that the named entity recognition method based on dictionaries is superior to the used machine learning approach. In conclusion, the present thesis provides techniques which enable an enhanced utilization of textual data, hence, supporting research in epigenomics and drug discovery

Unsupervised Information Extraction From Text Extraction and Clustering of Relations between Entities

L'extraction d'information non supervisée en domaine ouvert est une évolution récente de l'extraction d'information adaptée à des contextes dans lesquels le besoin informationnel est faiblement spécifié. Dans ce cadre, la thèse se concentre plus particulièrement sur l'extraction et le regroupement de relations entre entités en se donnant la possibilité de traiter des volumes importants de données.L'extraction de relations se fixe plus précisément pour objectif de faire émerger des relations de type non prédéfini à partir de textes. Ces relations sont de nature semi-structurée : elles associent des éléments faisant référence à des structures de connaissance définies a priori, dans le cas présent les entités qu elles relient, et des éléments donnés uniquement sous la forme d une caractérisation linguistique, en l occurrence leur type. Leur extraction est réalisée en deux temps : des relations candidates sont d'abord extraites sur la base de critères simples mais efficaces pour être ensuite filtrées selon des critères plus avancés. Ce filtrage associe lui-même deux étapes : une première étape utilise des heuristiques pour éliminer rapidement les fausses relations en conservant un bon rappel tandis qu'une seconde étape se fonde sur des modèles statistiques pour raffiner la sélection des relations candidates.Le regroupement de relations a quant à lui un double objectif : d une part, organiser les relations extraites pour en caractériser le type au travers du regroupement des relations sémantiquement équivalentes et d autre part, en offrir une vue synthétique. Il est réalisé dans le cas présent selon une stratégie multiniveau permettant de prendre en compte à la fois un volume important de relations et des critères de regroupement élaborés. Un premier niveau de regroupement, dit de base, réunit des relations proches par leur expression linguistique grâce à une mesure de similarité vectorielle appliquée à une représentation de type sac-de-mots pour former des clusters fortement homogènes. Un second niveau de regroupement est ensuite appliqué pour traiter des phénomènes plus sémantiques tels que la synonymie et la paraphrase et fusionner des clusters de base recouvrant des relations équivalentes sur le plan sémantique. Ce second niveau s'appuie sur la définition de mesures de similarité au niveau des mots, des relations et des clusters de relations en exploitant soit des ressources de type WordNet, soit des thésaurus distributionnels. Enfin, le travail illustre l intérêt de la mise en œuvre d un clustering des relations opéré selon une dimension thématique, en complément de la dimension sémantique des regroupements évoqués précédemment. Ce clustering est réalisé de façon indirecte au travers du regroupement des contextes thématiques textuels des relations. Il offre à la fois un axe supplémentaire de structuration des relations facilitant leur appréhension globale mais également le moyen d invalider certains regroupements sémantiques fondés sur des termes polysémiques utilisés avec des sens différents. La thèse aborde également le problème de l'évaluation de l'extraction d'information non supervisée par l'entremise de mesures internes et externes. Pour les mesures externes, une méthode interactive est proposée pour construire manuellement un large ensemble de clusters de référence. Son application sur un corpus journalistique de grande taille a donné lieu à la construction d'une référence vis-à-vis de laquelle les différentes méthodes de regroupement proposées dans la thèse ont été évaluées.Unsupervised information extraction in open domain gains more and more importance recently by loosening the constraints on the strict definition of the extracted information and allowing to design more open information extraction systems. In this new domain of unsupervised information extraction, this thesis focuses on the tasks of extraction and clustering of relations between entities at a large scale. The objective of relation extraction is to discover unknown relations from texts. A relation prototype is first defined, with which candidates of relation instances are initially extracted with a minimal criterion. To guarantee the validity of the extracted relation instances, a two-step filtering procedures is applied: the first step with filtering heuristics to remove efficiently large amount of false relations and the second step with statistical models to refine the relation candidate selection. The objective of relation clustering is to organize extracted relation instances into clusters so that their relation types can be characterized by the formed clusters and a synthetic view can be offered to end-users. A multi-level clustering procedure is design, which allows to take into account the massive data and diverse linguistic phenomena at the same time. First, the basic clustering groups similar relation instances by their linguistic expressions using only simple similarity measures on a bag-of-word representation for relation instances to form high-homogeneous basic clusters. Second, the semantic clustering aims at grouping basic clusters whose relation instances share the same semantic meaning, dealing with more particularly phenomena such as synonymy or more complex paraphrase. Different similarities measures, either based on resources such as WordNet or distributional thesaurus, at the level of words, relation instances and basic clusters are analyzed. Moreover, a topic-based relation clustering is proposed to consider thematic information in relation clustering so that more precise semantic clusters can be formed. Finally, the thesis also tackles the problem of clustering evaluation in the context of unsupervised information extraction, using both internal and external measures. For the evaluations with external measures, an interactive and efficient way of building reference of relation clusters proposed. The application of this method on a newspaper corpus results in a large reference, based on which different clustering methods are evaluated.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Optimising Human-Machine Collaboration for Efficient High-Precision Information Extraction from Text Documents

From science to law enforcement, many research questions are answerable only by poring over a large amount of unstructured text documents. While people can extract information from such documents with high accuracy, this is often too time-consuming to be practical. On the other hand, automated approaches produce nearly-immediate results, but are not reliable enough for applications where near-perfect precision is essential. Motivated by two use cases from criminal justice, we consider the benefits and drawbacks of various human-only, human–machine, and machine-only approaches. Finding no tool well suited for our use cases, we develop a human-in-the-loop method for fast but accurate extraction of structured data from unstructured text. The tool is based on automated extraction followed by human validation, and is particularly useful in cases where purely manual extraction is not practical. Testing on three criminal justice datasets, we find that the combination of the computer speed and human understanding yields precision comparable to manual annotation while requiring only a fraction of time, and significantly outperforms the precision of all fully automated baselines

Real-Time Event Analysis and Spatial Information Extraction From Text Using Social Media Data

Since the advent of websites that enable users to participate and interact with each other by sharing content in different forms, a plethora of possibly relevant information is at scientists\u27 fingertips. Consequently, this thesis elaborates on two distinct approaches to extract valuable information from social media data and sketches out the potential joint use case in the domain of natural disasters

DoMoRe – A recommender system for domain modeling

Domain modeling is an important activity in early phases of software projects to achieve a shared understanding of the problem field among project participants. Domain models describe concepts and relations of respective application fields using a modeling language and domain-specific terms. Detailed knowledge of the domain as well as expertise in model-driven development is required for software engineers to create these models. This paper describes DoMoRe, a system for automated modeling recommendations to support the domain modeling process. We describe an approach in which modeling benefits from formalized knowledge sources and information extraction from text. The system incorporates a large network of semantically related terms built from natural language data sets integrated with mediator-based knowledge base querying in a single recommender system to provide context-sensitive suggestions of model elements