Qualities, objects, sorts, and other treasures : gold digging in English and Arabic

In the present monograph, we will deal with questions of lexical typology in the nominal domain. By the term "lexical typology in the nominal domain", we refer to crosslinguistic regularities in the interaction between (a) those areas of the lexicon whose elements are capable of being used in the construction of "referring phrases" or "terms" and (b) the grammatical patterns in which these elements are involved. In the traditional analyses of a language such as English, such phrases are called "nominal phrases". In the study of the lexical aspects of the relevant domain, however, we will not confine ourselves to the investigation of "nouns" and "pronouns" but intend to take into consideration all those parts of speech which systematically alternate with nouns, either as heads or as modifiers of nominal phrases. In particular, this holds true for adjectives both in English and in other Standard European Languages. It is well known that adjectives are often difficult to distinguish from nouns, or that elements with an overt adjectival marker are used interchangeably with nouns, especially in particular semantic fields such as those denoting MATERIALS or NATlONALlTIES. That is, throughout this work the expression "lexical typology in the nominal domain" should not be interpreted as "a typology of nouns", but, rather, as the cross-linguistic investigation of lexical areas constitutive for "referring phrases" irrespective of how the parts-of-speech system in a specific language is defined

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Personal named entity linking based on simple partial tree matching and context free grammar

Personal name disambiguation is the task of linking a personal name to a unique comparable entry in the real world, also known as named entity linking (NEL). Algorithms for NEL consist of three main components: extractor, searcher, and disambiguator. Existing approaches for NEL use exact-matched look-up over the surface form to generate a set of candidate entities in each of the mentioned names. The exact-matched look-up is wholly inadequate to generate a candidate entity due to the fact that the personal names within a web page lack uniform representation. In addition, the performance of a disambiguator in ranking candidate entities is limited by context similarity. Context similarity is an inflexible feature for personal disambiguation because natural language is highly variable. We propose a new approach that can be used to both identify and disambiguate personal names mentioned on a web page. Our NEL algorithm uses: as an extractor: a control flow graph; AlchemyAPI, as a searcher: Personal Name Transformation Modules (PNTM) based on Context Free Grammar and the Jaro-Winkler text similarity metric and as a disambiguator: the entity coherence method: the Occupation Architecture for Personal Name Disambiguation (OAPnDis), personal name concepts and Simple Partial Tree Matching (SPTM). Experimental results, evaluated on real-world data sets, show that the accuracy of our NEL is 92%, which is higher than the accuracy of previously used methods

24th International Conference on Information Modelling and Knowledge Bases

In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok

Joint models for information and knowledge extraction

Information and knowledge extraction from natural language text is a key asset for question answering, semantic search, automatic summarization, and other machine reading applications. There are many sub-tasks involved such as named entity recognition, named entity disambiguation, co-reference resolution, relation extraction, event detection, discourse parsing, and others. Solving these tasks is challenging as natural language text is unstructured, noisy, and ambiguous. Key challenges, which focus on identifying and linking named entities, as well as discovering relations between them, include: • High NERD Quality. Named entity recognition and disambiguation, NERD for short, are preformed first in the extraction pipeline. Their results may affect other downstream tasks. • Coverage vs. Quality of Relation Extraction. Model-based information extraction methods achieve high extraction quality at low coverage, whereas open information extraction methods capture relational phrases between entities. However, the latter degrades in quality by non-canonicalized and noisy output. These limitations need to be overcome. • On-the-fly Knowledge Acquisition. Real-world applications such as question answering, monitoring content streams, etc. demand on-the-fly knowledge acquisition. Building such an end-to-end system is challenging because it requires high throughput, high extraction quality, and high coverage. This dissertation addresses the above challenges, developing new methods to advance the state of the art. The first contribution is a robust model for joint inference between entity recognition and disambiguation. The second contribution is a novel model for relation extraction and entity disambiguation on Wikipediastyle text. The third contribution is an end-to-end system for constructing querydriven, on-the-fly knowledge bases.Informations- und Wissensextraktion aus natürlichsprachlichen Texten sind Schlüsselthemen vieler wissensbassierter Anwendungen. Darunter fallen zum Beispiel Frage-Antwort-Systeme, semantische Suchmaschinen, oder Applikationen zur automatischen Zusammenfassung und zum maschinellem Lesen von Texten. Zur Lösung dieser Aufgaben müssen u.a. Teilaufgaben, wie die Erkennung und Disambiguierung benannter Entitäten, Koreferenzresolution, Relationsextraktion, Ereigniserkennung, oder Diskursparsen, durchgeführt werden. Solche Aufgaben stellen eine Herausforderung dar, da Texte natürlicher Sprache in der Regel unstrukturiert, verrauscht und mehrdeutig sind. Folgende zentrale Herausforderungen adressieren sowohl die Identifizierung und das Verknüpfen benannter Entitäten als auch das Erkennen von Beziehungen zwischen diesen Entitäten: • Hohe NERD Qualität. Die Erkennung und Disambiguierung benannter Entitäten (engl. "Named Entity Recognition and Disambiguation", kurz "NERD") wird in Extraktionspipelines in der Regel zuerst ausgeführt. Die Ergebnisse beeinflussen andere nachgelagerte Aufgaben. • Abdeckung und Qualität der Relationsextraktion. Modellbasierte Informationsextraktionsmethoden erzielen eine hohe Extraktionsqualität, bei allerdings niedriger Abdeckung. Offene Informationsextraktionsmethoden erfassen relationale Phrasen zwischen Entitäten. Allerdings leiden diese Methoden an niedriger Qualität durch mehrdeutige Entitäten und verrauschte Ausgaben. Diese Einschränkungen müssen überwunden werden. • On-the-Fly Wissensakquisition. Reale Anwendungen wie Frage-Antwort- Systeme, die Überwachung von Inhaltsströmen usw. erfordern On-the-Fly Wissensakquise. Die Entwicklung solcher ganzheitlichen Systeme stellt eine hohe Herausforderung dar, da ein hoher Durchsatz, eine hohe Extraktionsqualität sowie eine hohe Abdeckung erforderlich sind. Diese Arbeit adressiert diese Probleme und stellt neue Methoden vor, um den aktuellen Stand der Forschung zu erweitern. Diese sind: • Ein robustesModell zur integrierten Inferenz zur gemeinschaftlichen Erkennung und Disambiguierung von Entitäten. • Ein neues Modell zur Relationsextraktion und Disambiguierung von Wikipedia-ähnlichen Texten. • Ein ganzheitliches System zur Erstellung Anfrage-getriebener On-the-Fly Wissensbanken

Formal Linguistic Models and Knowledge Processing. A Structuralist Approach to Rule-Based Ontology Learning and Population

2013 - 2014The main aim of this research is to propose a structuralist approach for knowledge processing by means of ontology learning and population, achieved starting from unstructured and structured texts. The method suggested includes distributional semantic approaches and NL formalization theories, in order to develop a framework, which relies upon deep linguistic analysis... [edited by author]XIII n.s

Can humain association norm evaluate latent semantic analysis?

This paper presents the comparison of word association norm created by a psycholinguistic experiment to association lists generated by algorithms operating on text corpora. We compare lists generated by Church and Hanks algorithm and lists generated by LSA algorithm. An argument is presented on how those automatically generated lists reflect real semantic relations

An Introduction to Ontologies and Ontology Engineering

In the last decades, the use of ontologies in information systems has become more and more popular in various fields, such as web technologies, database integration, multi agent systems, natural language processing, etc. Artificial intelligent researchers have initially borrowed the word “ontology” from Philosophy, then the word spread in many scientific domain and ontologies are now used in several developments. The main goal of this chapter is to answer generic questions about ontologies, such as: Which are the different kinds of ontologies? What is the purpose of the use of ontologies in an application? Which methods can I use to build an ontology