Nicoletta Calzolari

Building the Italian Syntactic-Semantic Treebank

The paper reports on the design and construction of a multi-layered corpus of Italian, annotated at the syntactic and lexico-semantic levels, whose development is supported by dedicated software augmented with an intelligent interface. The issue of evaluating this type of resource is also addressed.

Applying EuroWordNet to cross-language text retrieval

We discuss ways in which EuroWordNet (EWN) can be used in multilingual information retrieval activities, focusing on two approaches to Cross-Language Text Retrieval that use the EWN database as a large-scale multilingual semantic resource. The first approach indexes documents and queries in terms of the EuroWordNet Inter-Lingual-Index, thus turning term weighting and query/document matching into language-independent tasks. The second describes how the information in the EWN database could be integrated with a corpus-based technique, thus allowing retrieval of domain-specific terms that may not be present in our multilingual database. Our objective is to show the potential of EuroWordNet as a promising alternative to existing approaches to Cross-Language Text Retrieval.

Multilingual resources for NLP in the lexical markup framework (LMF)

Optimizing the production, maintenance and extension of lexical resources is one the crucial aspects impacting natural language processing (NLP). A second aspect involves optimizing the process leading to their integration in applications. With this respect, we believe that a consensual specification on monolingual, bilingual and multilingual lexicons can be a useful aid for the various NLP actors. Within ISO, one purpose of Lexical Markup Framework (LMF, ISO-24613) is to define a standard for lexicons that covers multilingual lexical data.

The linguistic design of the EuroWordNet database

In this paper the linguistic design of the database under construction within the EuroWordNet project is described. This is mainly structured along the same lines as the Princeton WordNet, although some changes have been made to the WordNet overall design due to both theoretical and practical reasons. The most important reasons for such changes are the multilinguality of the EuroWordNet database and the fact that it is intended to be used in Language Engineering applications. Thus, i) some relations have been added to those identified in WordNet; ii) some labels have been identified which can be added to the relations in order to make their implications more explicit and precise; iii) some relations, already present in the WordNet design, have been modified in order to specify their role more clearly.

The BioLexicon: A large-scale terminological resource for biomedical text mining

BackgroundDue to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.ResultsThis article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.ConclusionsThe BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.

Ontology and the lexicon : a natural language processing perspective

Part I. Fundamental Aspects: 1. Ontology and the lexicon: a multi-disciplinary perspective Laurent Prevot, Chu-Ren Huang, Nicoletta Calzolari, Aldo Gangemi, Alessandro Lenci and Alessandro Oltramari 2. Formal ontology as interlingua: the SUMO and WordNet linking project and GlobalWordNet Adam Pease and Christiane Fellbaum 3. Interfacing WordNet with DOLCE: towards OntoWordNet Aldo Gangemi, Nicola Guarino, Claudio Masolo and Alessandro Oltramari 4. Reasoning over natural language text by means of FrameNet and ontologies Jan Scheffczyk, Collin F. Baker and Srini Narayanan 5. Synergizing ontologies and the lexicon: a roadmap Alessandro Oltramari, Aldo Gangemi, Chu-Ren Huang, Nicoletta Calzolari, Alessandro Lenci and Laurent Prevot Part II. Discovery and Representation of Conceptual Systems: 6. Experiments of ontology construction with formal concept analysis SuJian Li, Qin Lu and Wenjie Li 7. Ontology, lexicon, and fact repository as leveraged to interpret events of change Marjorie McShane, Sergei Nirenburg and Stephen Beale 8. Hantology: conceptual system discovery based on orthographic convention Ya-Min Chou and Chu-Ren Huang 9. Whats in a schema? A formal metamodel for ECG and FrameNet Aldo Gangemi Part III. Interfacing Ontologies and Lexical Resources: 10. Interfacing ontologies and lexical resources Laurent Prevot, Stefano Borgo and Alessandro Oltramari 11. Sinica BOW (Bilingual Ontological WordNet): integration of BilingualWord-Net and SUMO Chu-Ren Huang, Ru-Yng Chang and Hsiang-bin Lee 12. Ontology-based semantic lexicons: mapping between terms and object descriptions Paul Buitelaar 13. Merging global and specialized linguistic ontologies Manuela Speranza and Bernardo Magnini Part IV. Learning and Using Ontological Knowledge: 14. The life cycle of knowledge Alessandro Lenci 15. The omega ontology Andrew Philpot, Eduard Hovy and Patrick Pantel 16. Automatic acquisition of lexico-semantic knowledge for question answering Lonneke van der Plas, Gosse Bouma and Jori Mur 17. Agricultural ontology construction and maintenance in Thai Asanee Kawtrakul and Aurawan Imsombut.The relation between ontologies and language is at the forefront of both natural language processing (NLP) and knowledge engineering. Ontologies, as widely used models in semantic technologies, have much in common with the lexicon. A lexicon organizes words as a conventional inventory of concepts, while an ontology formalizes concepts and their logical relations. A shared lexicon is the prerequisite for knowledge-sharing through language, and a shared ontology is the prerequisite for knowledge-sharing through information technology. In building models of language, computational linguists must be able to map accurately the relations between words and the concepts that they can be linked to. This book focuses on the integration of lexical resources and semantic technologies. It will be of interest to researchers and graduate students in NLP, computational linguistics and knowledge engineering, as well as in semantics, psycholinguistics, lexicology and morphology/syntax.

Acquiring and Representing Semantic Information in a Lexical Knowledge Base

The paper focuses on the description of the approach, taken within the ESPRIT BRA project ACQUILEX, towards: i) acquisition of semantic information from several machinereadable dictionaries (in four languages), and ii) its representation in a common Lexical Knowledge Base. Knowledge extraction is guided by a) empirical observations and b) theoretical hypotheses. As for representation, we stress the convergence of a) and b) towards the possibility of organizing the information extracted from MRDs in the form of ’meaning types’ or ’templates’, where a common metalanguage is used to encode conceptual and relational information. Examples taken from two Italian monolingual dictionaries and from LDOCE are given. Different uses of these templates (e.g. as guides in the semantic analysis of the definitions, as a structure for comparing, unifying, merging, integrating information coming from different sources and different languages, as a tool for correcting ’incoherences’ in dictionaries, etc.) are described.

Acquisition of semantic information from an on-line dictionary

After the first work on machine-readable dictionaries (MRDs) in the seventies, and with the recent development of the concept of a lexical database (LDB) in which interaction, flexibility and multidimensionality can be achieved, but everything must be explicitly stated in advance, a new possibility which is now emerging is that of a procedural exploitation of the full range of semantic information implicitly contained in MRDs. The dictionary is considered in this framework as a primary source of basic general knowledge. In the paper we describe a project to develop a system which has word-sense acquisition from information contained in computerized dictionaries and knowledge organization as its main objectives. The approach consists in a discovery procedure technique operating on natural language definitions, which is recursively applied and refined. We start from free-text definitions, in natural language linear form, analyzing and converting them into informationally equivalent structured forms. This new approach, which aims at reorganizing free text into elaborately structured information, could be called the Lexical Knowledge Base (LKB) approach.

Infrastructure for Standardization of Asian Language Resources

As an area of great linguistic and cultural diversity, Asian language resources have received much less attention than their western counterparts. Creating a common standard for Asian language resources that is compatible with an international standard has at least three strong advantages: to increase the competitive edge of Asian countries, to bring Asian countries to closer to their western counterparts, and to bring more cohesion among Asian countries. To achieve this goal, we have launched a two year project to create a common standard for Asian language resources. The project is comprised of four research items, (1) building a description framework of lexical entries, (2) building sample lexicons, (3) building an upper-layer ontology and (4) evaluating the proposed framework through an application. This paper outlines the project in terms of its aim and approach.

Working on the Italian machine dictionary: a semantic approach

The work described by the two co-authors of this article is presented with a double objective: apart from giving specific details on a particular project they also wished to provide a concrete example of the type of research which has been made possible by the Italian Machine Dictionary (DMI). The DMI is, in fact, one of the principal projects of the Linguistics Division (DL) of CNUCE. Other articles in the first volume of the Proceedings also refer to the DMI. 1 In this introduction I intend to indicate briefly how the DMI project, and, in particular, how the research described in the article has been inserted into the framework of the whole complex of activities of the DL and into our general conception of linguistic data processing (LDV). As I have already stated in my introduction to these Proceedings, 2 it is my conviction that, at this moment, special attention should be taken in order to promote, both on the theoretical and on the practical level, systematic and ordered interaction among the many different LPD activities. In particular, this cooperation should be realized between those activities which focus on the construction of theoretical models and those focussing on the processing of large corpora of linguistic data. The activity of the DL, especially in recent years, has been increasingly directed towards this goal.