Giulia Venturi

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.

NLP-based metadata extraction for legal text consolidation

The paper describes a system for the automatic consolidation of Italian legislative texts to be used as a support of an editorial consolidating activity and dealing with the following typology of textual amendments: repeal, substitution and integration. The focus of the paper is on the semantic analysis of the textual amendment provisions and the formalized representation of the amendments in terms of meta-data. The proposed approach to consolidation is metadata--oriented and based on Natural Language Processing (NLP) techniques: we use XML--based standards for metadata annotation of legislative acts and a flexible NLP architecture for extracting metadata from parsed texts. An evaluation of achieved results is also provided.

Bootstrapping a Verb Lexicon for Biomedical Information Extraction

The extraction of information from texts requires resources that contain both syntactic and semantic properties of lexical units. As the use of language in specialized domains, such as biology, can be very different to the general domain, there is a need for domain-specific resources to ensure that the information extracted is as accurate as possible. We are building a large-scale lexical resource for the biology domain, providing information about predicate-argument structure that has been bootstrapped from a biomedical corpus on the subject of E. Coli. The lexicon is currently focussed on verbs, and includes both automatically-extracted syntactic subcategorization frames, as well as semantic event frames that are based on annotation by domain experts. In addition, the lexicon contains manually-added explicit links between semantic and syntactic slots in corresponding frames. To our knowledge, this lexicon currently represents a unique resource within in the biomedical domain.

Domain Adaptation for Dependency Parsing at Evalita 2011

The domain adaptation task was aimed at investigating techniques for adapting state–of–the–art dependency parsing systems to new domains. Both the language dealt with, i.e. Italian, and the target domain, namely the legal domain, represent two main novelties of the task organised at Evalita 2011 with respect to previous domain adaptation initiatives. In this paper, we define the task and describe how the datasets were created from different resources. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results.

Assessing the Readability of Sentences: Which Corpora and Features?

The paper investigates the problem of sentence readability assessment, which is modelled as a classification task, with a specific view to text simplification. In particular, it addresses two open issues connected with it, i.e. the corpora to be used for training, and the identification of the most effective features to determine sentence readability. An existing readability assessment tool developed for Italian was specialized at the level of training corpus and learning algorithm. A maximum entropy–based feature selection and ranking algorithm (grafting) was used to identify to the most relevant features: it turned out that assessing the readability of sentences is a complex task, requiring a high number of features, mainly syntactic ones.

Legal language and legal knowledge management applications

This work is an investigation into the peculiarities of legal language with respect to ordinary language. Based on the idea that a shallow parsing approach can help to provide enough detailed linguistic information, this work presents the results obtained by shallow parsing (i.e. chunking) corpora of Italian and English legal texts and comparing them with corpora of ordinary language. In particular, this paper puts the emphasis of how understanding the syntactic and lexical characteristics of this specialised language has practical importance in the development of domain–specific Knowledge Management applications.

EvalIta 2011: The frame labeling over Italian texts task

The Frame Labeling over Italian Texts (FLaIT) task held within the EvalIta 2011 challenge is here described. It focuses on the automatic annotation of free texts according to frame semantics. Systems were asked to label all semantic frames and their arguments, as evoked by predicate words occurring in plain text sentences. Proposed systems are based on a variety of learning techniques and achieve very good results, over 80% of accuracy, in most subtasks.

Design and Annotation of the First Italian Corpus for Text Simplification

In this paper, we present design and construction of the first Italian corpus for automatic and semi‐automatic text simplification. In line with current approaches, we propose a new annotation scheme specifically conceived to identify the typology of changes an original sentence undergoes when it is manually simplified. Such a scheme has been applied to two aligned Italian corpora, containing original texts with corresponding simplified versions, selected as representative of two different manual simplification strategies and addressing different target reader populations. Each corpus was annotated with the operations foreseen in the annotation scheme, covering different levels of linguistic description. Annotation results were analysed with the final aim of capturing peculiarities and differences of the different simplification strategies pursued in the two corpora.

Extracting Dependency Relations from Digital Learning Content

Digital Libraries present tremendous potential for developing e-learning applications, such as text comprehension and question-answering tools. A way to build this kind of tools is structuring the digital content into relevant concepts and dependency relations among them. While the literature offers several approaches for the former, the identification of dependencies, and specifically of prerequisite relations, is still an open issue. We present an approach to manage this task.

PaCCSS-IT: A Parallel Corpus of Complex-Simple Sentences for Automatic Text Simplification.

In this paper we present PaCCSS–IT, a Parallel Corpus of Complex–Simple Sentences for ITalian. To build the resource we develop a new method for automatically acquiring a corpus of complex–simple paired sentences able to intercept structural transformations and particularly suitable for text simplification. The method requires a wide amount of texts that can be easily extracted from the web making it suitable also for less–resourced languages. We test it on the Italian language making available the biggest Italian corpus for automatic text simplification.