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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 audience; In 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.

WOS: 000430906100002, A morphological lexicon that is a computational source should be considered together with derivational morphology especially for agglutinative languages. To the best of our knowledge, in the Turkish language there has been no study that analyzes the derivational suffixes on the lexicon in a computational paradigm. This study provides a very rich lexical resource, filling a gap in the field, and would hopefully lead to new related studies as well. The morphological lexicon can be used in morphological analysis as well as in several other tasks, such as stemming and part of speech (POS) tagging. In this study, we introduce a morphological lexicon named TrLex and present its components, preparation processes and some statistics. We observed that more than half of the single-word lemmas (56.7%) are in the derived structure. Since the word formation in Turkish prefer the morphological processes, this number is higher than the rate of compound-type words (2.7%). As a result of the work, we obtained a knowledge-intensive data table including several fields such as form, structure, semantic information. We also extracted Lexical Markup Framework (LMF) formatted file containing only morphological and POS information and made the file freely available, Anadolu University [1410F415], This work was supported by Anadolu University, Fund of Scientific Research Projects [grant number 1410F415].

International audience; We present an extension of the adverbial entries of the French morphological lexicon DELA (Dictionnaires Electroniques du LADL / LADL electronic dictionaries). Adverbs were extracted from LGLex, a NLP-oriented syntactic resource for French, which in its turn contains all adverbs extracted from the Lexicon-Grammar tables of both simple adverbs ending in -ment (i.e., '-ly') (Molinier and Levrier, 2000) and compound adverbs (Gross, 1986b; Gross, 1986a). This work exploits fine-grained linguistic information provided in existing resources. The resulting resource is reviewed in order to delete duplicates and is freely available under the LGPL-LR license.

The first version of the Croatian Morphological Lexicon (HML) was developed as early as 1994 and was utilized in the implementation of various experiments and systems dealing with Croatian. Since the HML is frequently used both as a stand-alone application and as a module in many other systems for processing Croatian, the lexicon is constantly being updated to newer versions by manual inserting unknown wordforms (i.e. the corresponding 3- tuples of lemmas, wordforms and morphosyntactic tags) in batches. Current version of HML cosists of 110.000 lemmas and more than 4.000.000 lexicon entries. Due to limitations in availability of expert human annotators and various other constraints, the process of manual inspection, lemma assingment and inflectional pattern selection for unknown wordforms is a rather slow procedure. Accordingly, in this paper, we propose a generic approach to (semi-)automatic generation of new candidate lemmas for HML, their verification, assingment of inflectional patterns and finally creation and insertion of new lexicon entries to HML in a single processing pipeline.

This paper describes methods used for generating a morphological lexicon of organization entity names in Croatian. This resource is intended for two primary tasks: template-based natural language generation and named entity identification. The main problems concerning the lexicon generation are high level of inflection in Croatian and low linguistic quality of the primary resource containing named entities in normal form. The problem is divided into two subproblems concerning single- word and multi-word expressions. The single-word problem is solved by training a supervised learning algorithm called linear successive abstraction. With existing common language morphological resources and two simple hand-crafted rules backing up the algorithm, accuracy of 98.70% on the test set is achieved. The multi-word problem is solved through a semi- automated process for multi-word entities occurring in the first 10, 000 named entities. The generated multi-word lexicon will be used for natural language generation only while named entity identification will be solved algorithmically in forthcoming research. The single-word lexicon is capable of handling both tasks.