OFrLex: A Computational Morphological and Syntactic Lexicon for Old French

Due to COVID19 pandemic, the 12th edition is cancelled. The LREC 2020 Proceedings are available at http://www.lrec-conf.org/proceedings/lrec2020/index.htmlThe version 2 of the paper is an updated version with regard to the originally published version (minor corrections).International audienceIn this paper we describe our work on the development and enrichment of OFrLex, a freely available, large-coverage morphological and syntactic Old French lexicon. We rely on several heterogeneous language resources to extract structured and exploitable information. The extraction follows a semi-automatic procedure with substantial manual steps to respond to difficulties encountered while aligning lexical entries from distinct language resources. OFrLex aims at improving natural language processing tasks on Old French such as part-of-speech tagging and dependency parsing. We provide quantitative information on OFrLex and discuss its reliability. We also describe and evaluate a semi-automatic, word-embedding-based lexical enrichment process aimed at increasing the accuracy of the resource. Results of this extension technique will be manually validated in the near future, a step that will take advantage of OFrLex's viewing, searching and editing interface, which is already accessible online

ЛІНГВІСТЫЧНЫ АНАЛІЗ ДЛЯ БЕЛАРУСКАГА КОРПУСА ТЭКСТАЎ З ПРЫМЯНЕННЕМ МЕТАДАЎ АПРАЦОЎКІ НАТУРАЛЬНАЙ МОВЫ І МАШЫННАГА НАВУЧАННЯ

The article focuses on the problems existing in text-to-speech synthesis. Different morphological, lexical and syntactical elements were localized with the help of the Belarusian unit of NooJ program. Those types of errors, which occur in Belarusian texts, were analyzed and corrected. Language model and part of speech tagging model were built. The natural language processing of Belarusian corpus with the help of developed algorithm using machine learning was carried out. The precision of developed models of machine learning has been 80–90 %. The dictionary was enriched with new words for the further using it in the systems of Belarusian speech synthesis. Аналізуюцца праблемы лакалізацыі розных марфалагічных, лексічных і сінтаксічных элементаў з дапамогай беларускага модуля праграмы NooJ. У тым ліку выпраўляюцца памылкі, якія сустракаюцца ў беларускіх тэкстах, будуюцца мадэлі мовы і тэгіравання часцін мовы. Праводзіцца апрацоўка беларускага корпуса тэкстаў на натуральнай мове з дапамогай распрацаванага алгарытму з выкарыстаннем машыннага навучання

Representation and parsing of multiword expressions

This book consists of contributions related to the definition, representation and parsing of MWEs. These reflect current trends in the representation and processing of MWEs. They cover various categories of MWEs such as verbal, adverbial and nominal MWEs, various linguistic frameworks (e.g. tree-based and unification-based grammars), various languages including English, French, Modern Greek, Hebrew, Norwegian), and various applications (namely MWE detection, parsing, automatic translation) using both symbolic and statistical approaches

Current trends

Deep parsing is the fundamental process aiming at the representation of the syntactic structure of phrases and sentences. In the traditional methodology this process is based on lexicons and grammars representing roughly properties of words and interactions of words and structures in sentences. Several linguistic frameworks, such as Headdriven Phrase Structure Grammar (HPSG), Lexical Functional Grammar (LFG), Tree Adjoining Grammar (TAG), Combinatory Categorial Grammar (CCG), etc., offer different structures and combining operations for building grammar rules. These already contain mechanisms for expressing properties of Multiword Expressions (MWE), which, however, need improvement in how they account for idiosyncrasies of MWEs on the one hand and their similarities to regular structures on the other hand. This collaborative book constitutes a survey on various attempts at representing and parsing MWEs in the context of linguistic theories and applications

The enrichment of lexical resources through incremental parsebanking

Automatic syntactic analysis of a corpus requires detailed lexical and morphological information that cannot always be harvested from traditional dictionaries. Therefore the development of a treebank presents an opportunity to simultaneously enrich the lexicon. In building NorGramBank, we use an incremental parsebanking approach, in which a corpus is parsed and disambiguated, and after improvements to the grammar and the lexicon, reparsed. In this context we have implemented a text preprocessing interface where annotators can enter unknown words or missing lexical information either before parsing or during disambiguation. The information added to the lexicon in this way may be of great interest both to lexicographers and to other language technology efforts