Issues on the acquisition of italian complex nominal from text corpora: a computational approach combining syntactic and semantic information

No abstract availabl

How predictive are grammatical constructions in Italian? The case of the caused-motion construction.

Abstract for oral presentation

Development and representation of Italian light-fare constructions

The essay describes the study of the development and use of light fare 'do' constructions in Child-directed Speech and in Child Language with the twofold goal of showing that a Construction Grammar approach is viable, and of providing support to usage-based, functional predictions on language acquisition. The analysis of naturalistic data derived from the CHILDES database lead to two main findings: first, a representation of fare Light Verb Constructions as a family of constructions organized like a radial category is not only possible but more explicative, second, there exists a 'fare' pivot schema that children generalize at an early stage because it serves the purpose of naming new events, activities or situations

A MWE Acquisition and Lexicon Builder Web Service

This paper describes the development of a web-service tool for the automatic extraction of Multi-word expressions lexicons, which has been integrated in a distributed platform for the automatic creation of linguistic resources. The main purpose of the work described is thus to provide a (computationally "light") tool that produces a full lexical resource: multi-word terms/items with relevant and useful attached information that can be used for more complex processing tasks and applications (e.g. parsing, MT, IE, query expansion, etc.). The output of our tool is a MW lexicon formatted and encoded in XML according to the Lexical Mark-up Framework. The tool is already functional and available as a service. Evaluation experiments show that the tool precision is of about 80%

Interoperability Framework: The FLaReNet action plan proposal

Standards are fundamental to ex-change, preserve, maintain and integrate data and language resources, and as an essential basis of any language resource infrastructure. This paper promotes an Interoperability Framework as a dynamic environment of standards and guidelines, also intended to support the provision of language-(web)service interoperability. In the past two decades, the need to define common practices and formats for linguistic resources has been increasingly recognized and sought. Today open, collaborative, shared data is at the core of a sound language strategy, and standardisation is actively on the move. This paper first describes the current landscape of standards, and presents the major barriers to their adoption; then, it describes those scenarios that critically involve the use of standards and provide a strong motivation for their adoption; lastly, a series of actions and steps needed to operationalise standards and achieve a full interoperability for Language Resources and Technologies are proposed

Using LMF to Shape a Lexicon for the Biomedical Domain

This paper describes the design, implementation and population of the BioLexicon in the framework of BootStrep, an FP6 project. The BioLexicon (BL) is a lexical resource designed for text mining in the bio-domain. It has been conceived to meet both domain requirements and upcoming ISO standards for lexical representation. The data model and data categories are compliant to the ISO Lexical Markup Framework and the Data Category Registry. The BioLexicon integrates features of lexicons and terminologies: term entries (and variants) derived from existing resources are enriched with linguistic features, including sub-categorization and predicate-argument information, extracted from texts. Thus, it is an extendable resource. Furthermore, the lexical entries will be aligned to concepts in the BioOntology, the ontological resource of the project. The BL implementation is an extensible relational database with automatic population procedures. Population relies on a dedicated input data structure allowing to upload terms and their linguistic properties and ?pull-and-push? them in the database. The BioLexicon teaches that the state-of-the-art is mature enough to aim at setting up a standard in this domain. Being conformant to lexical standards, the BioLexicon is interoperable and portable to other areas

A lexicon for biology and bioinformatics: the BOOTStrep experience

This paper describes the design, implementation and population of a lexical resource for biology and bioinformatics (the BioLexicon) developed within an ongoing European project. The aim of this project is text-based knowledge harvesting for support to information extraction and text mining in the biomedical domain. The BioLexicon is a large-scale lexical-terminological resource encoding different information types in one single integrated resource. In the design of the resource we follow the ISO/DIS 24613 ?Lexical Mark-up Framework? standard, which ensures reusability of the information encoded and easy exchange of both data and architecture. The design of the resource also takes into account the needs of our text mining partners who automatically extract syntactic and semantic information from texts and feed it to the lexicon. The present contribution first describes in detail the model of the BioLexicon along its three main layers: morphology, syntax and semantics; then, it briefly describes the database implementation of the model and the population strategy followed within the project, together with an example. The BioLexicon database in fact comes equipped with automatic uploading procedures based on a common exchange XML format, which guarantees that the lexicon can be properly populated with data coming from different sources

Bio-Lexicon DataBase: Achitecture, Concepts and Loading Software

No abstract availabl

D6.2 Integrated Final Version of the Components for Lexical Acquisition

The PANACEA project has addressed one of the most critical bottlenecks that threaten the development of technologies to support multilingualism in Europe, and to process the huge quantity of multilingual data produced annually. Any attempt at automated language processing, particularly Machine Translation (MT), depends on the availability of language-specific resources. Such Language Resources (LR) contain information about the language\u27s lexicon, i.e. the words of the language and the characteristics of their use. In Natural Language Processing (NLP), LRs contribute information about the syntactic and semantic behaviour of words - i.e. their grammar and their meaning - which inform downstream applications such as MT. To date, many LRs have been generated by hand, requiring significant manual labour from linguistic experts. However, proceeding manually, it is impossible to supply LRs for every possible pair of European languages, textual domain, and genre, which are needed by MT developers. Moreover, an LR for a given language can never be considered complete nor final because of the characteristics of natural language, which continually undergoes changes, especially spurred on by the emergence of new knowledge domains and new technologies. PANACEA has addressed this challenge by building a factory of LRs that progressively automates the stages involved in the acquisition, production, updating and maintenance of LRs required by MT systems. The existence of such a factory will significantly cut down the cost, time and human effort required to build LRs. WP6 has addressed the lexical acquisition component of the LR factory, that is, the techniques for automated extraction of key lexical information from texts, and the automatic collation of lexical information into LRs in a standardized format. The goal of WP6 has been to take existing techniques capable of acquiring syntactic and semantic information from corpus data, improving upon them, adapting and applying them to multiple languages, and turning them into powerful and flexible techniques capable of supporting massive applications. One focus for improving the scalability and portability of lexical acquisition techniques has been to extend exiting techniques with more powerful, less "supervised" methods. In NLP, the amount of supervision refers to the amount of manual annotation which must be applied to a text corpus before machine learning or other techniques are applied to the data to compile a lexicon. More manual annotation means more accurate training data, and thus a more accurate LR. However, given that it is impractical from a cost and time perspective to manually annotate the vast amounts of data required for multilingual MT across domains, it is important to develop techniques which can learn from corpora with less supervision. Less supervised methods are capable of supporting both large-scale acquisition and efficient domain adaptation, even in the domains where data is scarce. Another focus of lexical acquisition in PANACEA has been the need of LR users to tune the accuracy level of LRs. Some applications may require increased precision, or accuracy, where the application requires a high degree of confidence in the lexical information used. At other times a greater level of coverage may be required, with information about more words at the expense of some degree of accuracy. Lexical acquisition in PANACEA has investigated confidence thresholds for lexical acquisition to ensure that the ultimate users of LRs can generate lexical data from the PANACEA factory at the desired level of accuracy

A LexO-Server Use Case: Languages and Cultures of Ancient Italy

Purpose: This paper presents a set of REST services called a LexO-server (https://github.com/andreabellandi/LexO-) for the management of lexical resources modeled as the OntoLex-Lemon model. This comes as a software backend, providing data access and manipulation to frontend developers, and will be exemplified through the Languages and Cultures of Ancient Italy CLARINIT related project. This is a use case where the creation and edition of an integrated system of LRs for ancient fragmentary languages will be shown in compliance with current digital humanities and Linked Open Data principles. Other relevant use cases will be mentioned for demonstrating the versatility of these services and how they can be easily integrated within more complex systems and/or interact with other independent back-ends