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“Linguistic Landscapes” (LL) is a research method which has become increasingly popular in recent years. In this paper, we will first explain the method itself and discuss some of its fundamental assumptions. We will then recall the basic traits of multilingualism in the Baltic States, before presenting results from our project carried out together with a group of Master students of Philology in several medium-sized towns in the Baltic States, focussing on our home town of Rēzekne in the highly multilingual region of Latgale in Eastern Latvia. In the discussion of some of the results, we will introduce the concept of “Legal Hypercorrection” as a term for the stricter compliance of language laws than necessary. The last part will report on advantages of LL for educational purposes of multilingualism, and for developing discussions on multilingualism among the general public.
Though polarity classification has been extensively explored at document level, there has been little work investigating feature design at sentence level. Due to the small number of words within a sentence, polarity classification at sentence level differs substantially from document-level classification in that resulting bag-of-words feature vectors tend to be very sparse resulting in a lower classification accuracy.
In this paper, we show that performance can be improved by adding features specifically designed for sentence-level polarity classification. We consider both explicit polarity information and various linguistic features. A great proportion of the improvement that can be obtained by using polarity information can also be achieved by using a set of simple domain-independent linguistic features.
TePaCoC - A Testsuite for Testing Parser Performance on Complex German Grammatical Constructions
(2009)
TEI Feature Structures as a Representation Format for Multiple Annotation and Generic XML Documents
(2009)
Feature structures are mathematical entities (rooted labeled directed acyclic graphs) that can be represented as graph displays, attribute value matrices or as XML adhering to the constraints of a specialized TEI tag set. We demonstrate that this latter ISO-standardized format can be used as an integrative storage and exchange format for sets of multiple annotation XML documents. This specific domain of application is rooted in the approach of multiple annotations, which marks a possible solution for XML-compliant markup in scenarios with conflicting annotation hierarchies. A more extreme proposal consists in the possible use as a meta-representation format for generic XML documents. For both scenarios our strategy concerning pertinent feature structure representations is grounded on the XDM (XQuery 1.0 and XPath 2.0 Data Model). The ubiquitous hierarchical and sequential relationships within XML documents are represented by specific features that take ordered list values. The mapping to the TEI feature structure format has been implemented in the form of an XSLT 2.0 stylesheet. It can be characterized as exploiting aspects of both the push and pull processing paradigm as appropriate. An indexing mechanism is provided with regard to the multiple annotation documents scenario. Hence, implicit links concerning identical primary data are made explicit in the result format. In comparison to alternative representations, the TEI-based format does well in many respects, since it is both integrative and well-formed XML. However, the result documents tend to grow very large depending on the size of the input documents and their respective markup structure. This may also be considered as a downside regarding the proposed use for generic XML documents. On the positive side, it may be possible to achieve a hookup to methods and applications that have been developed for feature structure representations in the fields of (computational) linguistics and knowledge representation.
The paper discusses particular logical consistency conditions satisfied by German proposition-embedding predicates which determine the question type (external and internal whether-form as well as exhaustive and non-exhaustive wh-form), the correlate type (es- or da-correlate) as well as the impact of the correlate on the respective consistency condition. It will turn out that some consistency conditions also determine the embedding of verb second and subject-control.
Generative lexicalized parsing models, which are the mainstay for probabilistic parsing of English, do not perform as well when applied to languages with different language-specific properties such as free(r) word order or rich morphology. For German and other non-English languages, linguistically motivated complex treebank transformations have been shown to improve performance within the framework of PCFG parsing, while generative lexicalized models do not seem to be as easily adaptable to these languages. In this paper, we show a practical way to use grammatical functions as first-class citizens in a discriminative model that allows to extend annotated treebank grammars with rich feature sets without having to suffer from sparse data problems. We demonstrate the flexibility of the approach by integrating unsupervised PP attachment and POS-based word clusters into the parser.
In opinion mining, there has been only very little work investigating semi-supervised machine learning on document-level polarity classification. We show that semi-supervised learning performs significantly better than supervised learning when only few labelled data are available. Semi-supervised polarity classifiers rely on a predictive feature set. (Semi-)Manually built polarity lexicons are one option but they are expensive to obtain and do not necessarily work in an unknown domain. We show that extracting frequently occurring adjectives & adverbs of an unlabeled set of in-domain documents is an inexpensive alternative which works equally well throughout different domains.
Digitale Medien haben in einer rasenden Geschwindigkeit inzwischen alle Lebensbereiche verändert. Sie greifen immer weiter in gewachsene Strukturen ein und prägen immer mehr unsere Wirtschafts-, Arbeits- und Sozialwelt, aber auch unsere private Kommunikation und unser alltägliches Leben. Ständig neue Entwicklungen stellen dabei alle Beteiligten immer wieder vor neue Herausforderungen. Damit einher geht die Notwendigkeit, sich kontinuierlich neues Wissen anzueignen. Als Schlüsselqualifikation zur Beherrschung dieser neuen Anforderungen in unserer sich ständig ändernden Gesellschaft gilt Medienkompetenz. Neben Lesen, Schreiben und Rechnen ist sie zur vierten Kulturtechnik geworden, die alle Bürgerinnen und Bürger in unserer Gesellschaft unabhängig von Alter, Geschlecht und Herkunft beherrschen sollten. Um an den aktuellen gesellschaftlichen und politischen Entwicklungen überhaupt noch teilnehmen und erwerbsfähig bleiben zu können, muss diese Kompetenz sogar beherrscht werden können. Damit wird ihre Vermittlung zum staatlichen Bildungsauftrag.
We present MaJo, a toolkit for supervised Word Sense Disambiguation (WSD), with an interface for Active Learning. Our toolkit combines a flexible plugin architecture which can easily be extended, with a graphical user interface which guides the user through the learning process. MaJo integrates off-the-shelf NLP tools like POS taggers, treebank-trained statistical parsers, as well as linguistic resources like WordNet and GermaNet. It enables the user to systematically explore the benefit gained from different feature types for WSD. In addition, MaJo provides an Active Learning environment, where the
system presents carefully selected instances to a human oracle. The toolkit supports manual annotation of the selected instances and re-trains the system on the extended data set. MaJo also provides the means to evaluate the performance of the system against a gold standard. We illustrate the usefulness of our system by learning the frames (word senses) for three verbs from the SALSA corpus, a version of the TiGer treebank with an additional layer of frame-semantic annotation. We show how MaJo can be used to tune the feature set for specific target words and so improve performance for these targets. We also show that syntactic features, when carefully tuned to the target word, can lead to a substantial increase in performance.
This paper introduces LRTwiki, an improved variant of the Likelihood Ratio Test (LRT). The central idea of LRTwiki is to employ a comprehensive domain specific knowledge source as additional “on-topic” data sets, and to modify the calculation of the LRT algorithm to take advantage of this new information. The knowledge source is created on the basis of Wikipedia articles. We evaluate on the two related tasks product feature extraction and keyphrase extraction, and find LRTwiki to yield a significant improvement over the original LRT in both tasks.
2008. godā tyka veikts pietejums, kura golvonais mierkis beja raksturuot niulenejū latgalīšu volūdys lūmu izgleiteibys sistemā. Itys roksts prezeņtej byutiskuokūs pietejuma rezultatus. Pietejuma īrūsme sajimta nu „Mercator Education Centre“ (Merkatora izgleiteibys centra), kas dorbojās Nīderlaņdē Ļuvortā (frīzu volūdā — Ljouwert), Frīzejis proviņcis golvyspiļsātā. Piļneigs pietejuma izvārsums ar Merkatora izgleiteibys centra atbolstu publicāts izdavumu serejā „Regional Dossier Series“ (Regionalūs dosje sereja) angļu volūdā. Itys roksts golvonom kuortom dūmuots taidam adresatam, kas mozuok ir saisteits ar Eiropys volūdu izpietis institucejom i kam roksti angļu volūdā var saguoduot izpratnis voi atrasšonys gryuteibys. Partū pietejuma suokumā teik dūts seikuoks metožu i mierķu raksturuojums, paskaidrojūt pietejuma strukturu i rezultatu apkūpuojuma veidu, kai ari dūts puorskots par latgalīšu volūdys lūmu myusdīnu izgleiteibys sistemā. Sacynuojumūs ir īzeimātys nuokūtnis perspektivis i prīšklykumi dabuotūs rezultatu izmontuojumam.
In this paper we present an approach to faceted search in large language resource repositories. This kind of search which enables users to browse through the repository by choosing their personal sequence of facets heavily relies on the availability of descriptive metadata for the objects in the repository. This approach therefore informs the collection of a minimal set of metatdata for language resources. The work described in this paper has been funded by the EC within the ESFRI infrastructure project CLARIN.
From Proof Texts to Logic. Discourse Representation Structures for Proof Texts in Mathematics
(2009)
We present an extension to Discourse Representation Theory that can be used to analyze mathematical texts written in the commonly used semi-formal language of mathematics (or at least a subset of it). Moreover, we describe an algorithm that can be used to check the resulting Proof Representation Structures for their logical validity and adequacy as a proof.
Cette contribution discute différents enjeux dégagés lors d’une étude des pratiques professionnelles plurilingues : ces enjeux ont émergé d’une analyse menée collaborativement par deux équipes de chercheurs, à Lyon et à Paris, participant au projet européen DYLAN (6e programme cadre) et élaborant ensemble l’analyse empirique d’un extrait d’une réunion de travail, enregistrée dans le cadre d’une collaboration sur un même terrain. Cette analyse est l’occasion de thématiser de manière exemplaire un certain nombre de questions surgissant de l’étude des contacts des langues dans les contextes professionnels, concernant aussi bien les enjeux épistémologiques que l'engagement du chercheur sur le terrain.
Spoken language corpora— as used in conversation analytic research, language acquisition studies and dialectology— pose a number of challenges that are rarely addressed by corpus linguistic methodology and technology. This paper starts by giving an overview of the most important methodological issues distinguishing spoken language corpus workfrom the work with written data. It then shows what technological challenges these methodological issues entail and demonstrates how they are dealt with in the architecture and tools of the EXMARaLDA system.
Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations
(2009)
In this paper we show that the extraction of opinions from free-text reviews can improve the accuracy of movie recommendations. We present three approaches to extract movie aspects as opinion targets and use them as features for the collaborative filtering. Each of these approaches requires different amounts of manual interaction. We collected a data set of reviews with corresponding ordinal (star) ratings of several thousand movies to evaluate the different features for the collaborative filtering. We employ a state-of-the-art collaborative filtering engine for the recommendations during our evaluation and compare the performance with and without using the features representing user preferences mined from the free-text reviews provided by the users. The opinion mining based features perform significantly better than the baseline, which is based on star ratings and genre information only.