Using distributional similarity to organise biomedical terminology

We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy

Understanding the structure and meaning of Finnish texts: From corpus creation to deep language modelling

Natural Language Processing (NLP) is a cross-disciplinary field combining elements of computer science, artificial intelligence, and linguistics, with the objective of developing means for computational analysis, understanding or generation of human language. The primary aim of this thesis is to advance natural language processing in Finnish by providing more resources and investigating the most effective machine learning based practices for their use. The thesis focuses on NLP topics related to understanding the structure and meaning of written language, mainly concentrating on structural analysis (syntactic parsing) as well as exploring the semantic equivalence of statements that vary in their surface realization (paraphrase modelling). While the new resources presented in the thesis are developed for Finnish, most of the methodological contributions are language-agnostic, and the accompanying papers demonstrate the application and evaluation of these methods across multiple languages. The first set of contributions of this thesis revolve around the development of a state-of-the-art Finnish dependency parsing pipeline. Firstly, the necessary Finnish training data was converted to the Universal Dependencies scheme, integrating Finnish into this important treebank collection and establishing the foundations for Finnish UD parsing. Secondly, a novel word lemmatization method based on deep neural networks is introduced and assessed across a diverse set of over 50 languages. And finally, the overall dependency parsing pipeline is evaluated on a large number of languages, securing top ranks in two competitive shared tasks focused on multilingual dependency parsing. The overall outcome of this line of research is a parsing pipeline reaching state-of-the-art accuracy in Finnish dependency parsing, the parsing numbers obtained with the latest pre-trained language models approaching (at least near) human-level performance. The achievement of large language models in the area of dependency parsing— as well as in many other structured prediction tasks— brings up the hope of the large pre-trained language models genuinely comprehending language, rather than merely relying on simple surface cues. However, datasets designed to measure semantic comprehension in Finnish have been non-existent, or very scarce at the best. To address this limitation, and to reflect the general change of emphasis in the field towards task more semantic in nature, the second part of the thesis shifts its focus to language understanding through an exploration of paraphrase modelling. The second contribution of the thesis is the creation of a novel, large-scale, manually annotated corpus of Finnish paraphrases. A unique aspect of this corpus is that its examples have been manually extracted from two related text documents, with the objective of obtaining non-trivial paraphrase pairs valuable for training and evaluating various language understanding models on paraphrasing. We show that manual paraphrase extraction can yield a corpus featuring pairs that are both notably longer and less lexically overlapping than those produced through automated candidate selection, the current prevailing practice in paraphrase corpus construction. Another distinctive feature in the corpus is that the paraphrases are identified and distributed within their document context, allowing for richer modelling and novel tasks to be defined

Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing

Linguistic typology aims to capture structural and semantic variation across the world's languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-employment of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such approach could be facilitated by recent developments in data-driven induction of typological knowledge

The TXM Portal Software giving access to Old French Manuscripts Online

Texte intégral en ligne : http://www.lrec-conf.org/proceedings/lrec2012/workshops/13.ProceedingsCultHeritage.pdfInternational audiencehttp://www.lrec-conf.org/proceedings/lrec2012/workshops/13.ProceedingsCultHeritage.pdf This paper presents the new TXM software platform giving online access to Old French Text Manuscripts images and tagged transcriptions for concordancing and text mining. This platform is able to import medieval sources encoded in XML according to the TEI Guidelines for linking manuscript images to transcriptions, encode several diplomatic levels of transcription including abbreviations and word level corrections. It includes a sophisticated tokenizer able to deal with TEI tags at different levels of linguistic hierarchy. Words are tagged on the fly during the import process using IMS TreeTagger tool with a specific language model. Synoptic editions displaying side by side manuscript images and text transcriptions are automatically produced during the import process. Texts are organized in a corpus with their own metadata (title, author, date, genre, etc.) and several word properties indexes are produced for the CQP search engine to allow efficient word patterns search to build different type of frequency lists or concordances. For syntactically annotated texts, special indexes are produced for the Tiger Search engine to allow efficient syntactic concordances building. The platform has also been tested on classical Latin, ancient Greek, Old Slavonic and Old Hieroglyphic Egyptian corpora (including various types of encoding and annotations)

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010