Selección de un etiquetador morfosintáctico primando la precisión en las categorías léxicas

In this article, four Part-of-Speech (PoS) taggers for Spanish are compared. The evaluation has been carried out without prior training or tuning of the PoS taggers. To allow for a comparison across PoS taggers, their tagsets have been mapped to the universal PoS tagset (Petrov, Das, and McDonald, 2012). The PoS taggers have also been compared as regards the information they provide and how they treat special features of the Spanish language such as verbal clitics and portmanteaux.En este artículo se comparan cuatro etiquetadores morfosintácticos para el español. La evaluación se ha realizado sin entrenamiento ni adaptación previa de los etiquetadores. Para poder realizar la comparación, los etiquetarios se han convertido al etiquetario universal (Petrov, Das, and McDonald, 2012). También se han comparado los etiquetadores en cuanto a la información que facilitan y cómo tratan características intrínsecas del idioma español como los clíticos verbales y las contracciones

Unsupervised Acquisition of Verb Subcategorization Frames from Shallow-Parsed Corpora

In this paper, we reported experiments of unsupervised automatic acquisition of Italian and English verb subcategorization frames (SCFs) from general and domain corpora. The proposed technique operates on syntactically shallow-parsed corpora on the basis of a limited number of search heuristics not relying on any previous lexico-syntactic knowledge about SCFs. Although preliminary, reported results are in line with state-of-the-art lexical acquisition systems. The issue of whether verbs sharing similar SCFs distributions happen to share similar semantic properties as well was also explored by clustering verbs that share frames with the same distribution using the Minimum Description Length Principle (MDL). First experiments in this direction were carried out on Italian verbs with encouraging results

D6.1: Technologies and Tools for Lexical Acquisition

This report describes the technologies and tools to be used for Lexical Acquisition in PANACEA. It includes descriptions of existing technologies and tools which can be built on and improved within PANACEA, as well as of new technologies and tools to be developed and integrated in PANACEA platform. The report also specifies the Lexical Resources to be produced. Four main areas of lexical acquisition are included: Subcategorization frames (SCFs), Selectional Preferences (SPs), Lexical-semantic Classes (LCs), for both nouns and verbs, and Multi-Word Expressions (MWEs)

The syllable as a processing unit in speech production: evidence from frequency effects on coarticulation

Samlowski B. The syllable as a processing unit in speech production: evidence from frequency effects on coarticulation. Bielefeld: Universität Bielefeld; 2016

Mapping of electronic health records in Spanish to the unified medical language system metathesaurus

[EN] This work presents a preliminary approach to annotate Spanish electronic health records with concepts of the Unified Medical Language System Metathesaurus. The prototype uses Apache Lucene R to index the Metathesaurus and generate mapping candidates from input text. In addition, it relies on UKB to resolve ambiguities. The tool has been evaluated by measuring its agreement with MetaMap in two English-Spanish parallel corpora, one consisting of titles and abstracts of papers in the clinical domain, and the other of real electronic health record excerpts.[EU] Lan honetan, espainieraz idatzitako mediku-txosten elektronikoak Unified Medical Languge System Metathesaurus deituriko terminologia biomedikoarekin etiketatzeko lehen urratsak eman dira. Prototipoak Apache Lucene R erabiltzen du Metathesaurus-a indexatu eta mapatze hautagaiak sortzeko. Horrez gain, anbiguotasunak UKB bidez ebazten ditu. Ebaluazioari dagokionez, prototipoaren eta MetaMap-en arteko adostasuna neurtu da bi ingelera-gaztelania corpus paralelotan. Corpusetako bat artikulu zientifikoetako izenburu eta laburpenez osatutako dago. Beste corpusa mediku-txosten pasarte batzuez dago osatuta

Proceedings

Proceedings of the NODALIDA 2011 Workshop Constraint Grammar Applications. Editors: Eckhard Bick, Kristin Hagen, Kaili Müürisep, Trond Trosterud. NEALT Proceedings Series, Vol. 14 (2011), vi+69 pp. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/19231

Eesti keele üldvaldkonna tekstide laia kattuvusega automaatne sündmusanalüüs

Seoses tekstide suuremahulise digitaliseerimisega ning digitaalse tekstiloome järjest laiema levikuga on tohutul hulgal loomuliku keele tekste muutunud ja muutumas masinloetavaks. Masinloetavus omab potentsiaali muuta tekstimassiivid inimeste jaoks lihtsamini hallatavaks, nt lubada rakendusi nagu automaatne sisukokkuvõtete tegemine ja tekstide põhjal küsimustele vastamine, ent paraku ei ulatu praegused automaatanalüüsi võimalused tekstide sisu tegeliku mõistmiseni. Oletatakse, tekstide sisu mõistvale automaatanalüüsile viib meid lähemale sündmusanalüüs – kuna paljud tekstid on narratiivse ülesehitusega, tõlgendatavad kui „sündmuste kirjeldused”, peaks tekstidest sündmuste eraldamine ja formaalsel kujul esitamine pakkuma alust mitmete „teksti mõistmist” nõudvate keeletehnoloogia rakenduste loomisel. Käesolevas väitekirjas uuritakse, kuivõrd saab eestikeelsete tekstide sündmusanalüüsi käsitleda kui avatud sündmuste hulka ja üldvaldkonna tekste hõlmavat automaatse lingvistilise analüüsi ülesannet. Probleemile lähenetakse eesti keele automaatanalüüsi kontekstis uudsest, sündmuste ajasemantikale keskenduvast perspektiivist. Töös kohandatakse eesti keelele TimeML märgendusraamistik ja luuakse raamistikule toetuv automaatne ajaväljendite tuvastaja ning ajasemantilise märgendusega (sündmusviidete, ajaväljendite ning ajaseoste märgendusega) tekstikorpus; analüüsitakse korpuse põhjal inimmärgendajate kooskõla sündmusviidete ja ajaseoste määramisel ning lõpuks uuritakse võimalusi ajasemantika-keskse sündmusanalüüsi laiendamiseks geneeriliseks sündmusanalüüsiks sündmust väljendavate keelendite samaviitelisuse lahendamise näitel. Töö pakub suuniseid tekstide ajasemantika ja sündmusstruktuuri märgenduse edasiarendamiseks tulevikus ning töös loodud keeleressurssid võimaldavad nii konkreetsete lõpp-rakenduste (nt automaatne ajaküsimustele vastamine) katsetamist kui ka automaatsete märgendustööriistade edasiarendamist.  Due to massive scale digitalisation processes and a switch from traditional means of written communication to digital written communication, vast amounts of human language texts are becoming machine-readable. Machine-readability holds a potential for easing human effort on searching and organising large text collections, allowing applications such as automatic text summarisation and question answering. However, current tools for automatic text analysis do not reach for text understanding required for making these applications generic. It is hypothesised that automatic analysis of events in texts leads us closer to the goal, as many texts can be interpreted as stories/narratives that are decomposable into events. This thesis explores event analysis as broad-coverage and general domain automatic language analysis problem in Estonian, and provides an investigation starting from time-oriented event analysis and tending towards generic event analysis. We adapt TimeML framework to Estonian, and create an automatic temporal expression tagger and a news corpus manually annotated for temporal semantics (event mentions, temporal expressions, and temporal relations) for the language; we analyse consistency of human annotation of event mentions and temporal relations, and, finally, provide a preliminary study on event coreference resolution in Estonian news. The current work also makes suggestions on how future research can improve Estonian event and temporal semantic annotation, and the language resources developed in this work will allow future experimentation with end-user applications (such as automatic answering of temporal questions) as well as provide a basis for developing automatic semantic analysis tools

Statistical model of human lexical category disambiguation

Research in Sentence Processing is concerned with discovering the mechanism by which linguistic utterances are mapped onto meaningful representations within the human mind. Models of the Human Sentence Processing Mechanism (HSPM) can be divided into those in which such mapping is performed by a number of limited modular processes and those in which there is a single interactive process. A further, and increasingly important, distinction is between models which rely on innate preferences to guide decision processes and those which make use of experiencebased statistics. In this context, the aims of the current thesis are two-fold: • To argue that the correct architecture of the HSPM is both modular and statistical - the Modular Statistical Hypothesis (MSH). • To propose and provide empirical support for a position in which human lexical category disambiguation occurs within a modular process, distinct from syntactic parsing and guided by a statistical decision process. Arguments are given for why a modular statistical architecture should be preferred on both methodological and rational grounds. We then turn to the (often ignored) problem of lexical category disambiguation and propose the existence of a presyntactic Statistical Lexical Category Module (SLCM). A number of variants of the SLCM are introduced. By empirically investigating this particular architecture we also hope to provide support for the more general hypothesis - the MSH. The SLCM has some interesting behavioural properties; the remainder of the thesis empirically investigates whether these behaviours are observable in human sentence processing. We first consider whether the results of existing studies might be attributable to SLCM behaviour. Such evaluation provides support for an HSPM architecture that includes this SLCM and allows us to determine which SLCM variant is empirically most plausible. Predictions are made, using this variant, to determine SLCM behaviour in the face of novel utterances; these predictions are then tested using a self-paced reading paradigm. The results of this experimentation fully support the inclusion of the SLCM in a model of the HSPM and are not compatible with other existing models. As the SLCM is a modular and statistical process, empirical evidence for the SLCM also directly supports an HSPM architecture which is modular and statistical. We therefore conclude that our results strongly support both the SLCM and the MSH. However, more work is needed, both to produce further evidence and to define the model further