Total Articles: 38870

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15748

AbstractThe Open Linguistics Working Group (OWLG) brings together researchers from various fields of linguistics, natural language processing, and information technology to present and discuss principles, case studies, and best practices for representing, publishing and linking linguistic data collections. A major outcome of our work is the Linguistic Linked Open Data …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15749

AbstractThis paper presents the process of enriching the verb frame database of a Hungarian natural language parser to enable the assignment of semantic roles. We accomplished this by linking the parser’s verb frame database to existing linguistic resources such as VerbNet and WordNet, and automatically transferring back semantic knowledge. We …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15750

AbstractRecent research shows the importance of linking linguistic knowledge resources for the creation of large-scale linguistic data. We describe our approach for combining two English resources, FrameNet and sar-graphs, and illustrate the benefits of the linked data in a relation extraction setting. While FrameNet consists of schematic representations of situations, …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15751

AbstractThe paper presents the strategy and results of mapping adjective synsets between plWordNet (the wordnet of Polish, cf. Piasecki et al. 2009, Maziarz et al. 2013) and Princeton WordNet (cf. Fellbaum 1998). The main challenge of this enterprise has been very different synset relation structures in the two networks: horizontal, …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15839

AbstractIn knowledge bases where concepts have associated properties, there is a large amount of comparative information that is implicitly encoded in the values of the properties these concepts share. Although there have been previous approaches to generating riddles, none of them seem to take advantage of structured information stored in …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15840

AbstractWe present an attempt to link the large amount of different concept lists which are used in the linguistic literature, ranging from Swadesh lists in historical linguistics to naming tests in clinical studies and psycholinguistics. This resource, our Concepticon, links 30 222 concept labels from 160 conceptlists to 2495 concept …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15841

AbstractIn this study we elaborate a road map for the conversion of a traditional lexical syntactico-semantic resource for French into a linguistic linked open data (LLOD) model. Our approach uses current best-practices and the analyses of earlier similar undertakings (lemonUBY and PDEV-lemon) to tease out the most appropriate representation for …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15842

AbstractWe introduce a new member of the family of Prague dependency treebanks. The Czech Legal Text Treebank 1.0 is a morphologically and syntactically annotated corpus of 1,128 sentences. The treebank contains texts from the legal domain, namely the documents from the Collection of Laws of the Czech Republic. Legal texts …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15843

AbstractThis paper describes our efforts for the development of a Proposition Bank for Urdu, an Indo-Aryan language. Our primary goal is the labeling of syntactic nodes in the existing Urdu dependency Treebank with specific argument labels. In essence, it involves annotation of predicate argument structures of both simple and complex …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15844

AbstractMany shallow natural language understanding tasks use dependency trees to extract relations between content words. However, strict surface-structure dependency trees tend to follow the linguistic structure of sentences too closely and frequently fail to provide direct relations between content words. To mitigate this problem, the original Stanford Dependencies representation also …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15845

AbstractWe present the French Question Bank, a treebank of 2600 questions. We show that classical parsing model performance drop while the inclusion of this data set is highly beneficial without harming the parsing of non-question data. when facing out-of- domain data with strong structural diver- gences. Two thirds being aligned …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15846

AbstractThe Persian Universal Dependency Treebank (Persian UD) is a recent effort of treebanking Persian with Universal Dependencies (UD), an ongoing project that designs unified and cross-linguistically valid grammatical representations including part-of-speech tags, morphological features, and dependency relations. The Persian UD is the converted version of the Uppsala Persian Dependency Treebank …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15847

AbstractThis paper introduces EasyTree, a dynamic graphical tool for dependency tree annotation. Built in JavaScript using the popular D3 data visualization library, EasyTree allows annotators to construct and label trees entirely by manipulating graphics, and then export the corresponding data in JSON format. Human users are thus able to annotate …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15848

AbstractWikipedia, the well known internet encyclopedia, is nowadays a widely used source of information. To leverage its rich information, already parsed versions of Wikipedia have been proposed. We present an annotated dump of the English Wikipedia. This dump draws upon previously released Wikipedia parsed dumps. Still, we head in a …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15849

AbstractTo ensure portability of NLP systems across multiple domains, existing treebanks are often extended by adding trees from interesting domains that were not part of the initial annotation effort. In this paper, we will argue that it is both useful from an application viewpoint and enlightening from a linguistic viewpoint …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15850

AbstractMultiword Expressions (MWEs) are used frequently in natural languages, but understanding the diversity in MWEs is one of the open problem in the area of Natural Language Processing. In the context of Indian languages, MWEs play an important role. In this paper, we present MWEs annotation dataset created for Indian …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15851

AbstractThe question of how to compare languages and more generally the domain of linguistic typology, relies on the study of different linguistic properties or phenomena. Classically, such a comparison is done semi-manually, for example by extracting information from databases such as the WALS. However, it remains difficult to identify precisely …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15852

AbstractBy means of an online survey, we have investigated ways in which various types of multiword expressions are annotated in existing treebanks. The results indicate that there is considerable variation in treatments across treebanks and thereby also, to some extent, across languages and across theoretical frameworks. The comparison is focused …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15854

AbstractCollocations such as “heavy rain” or “make [a] decision”, are combinations of two elements where one (the base) is freely chosen, while the choice of the other (collocate) is restricted, depending on the base. Collocations present difficulties even to advanced language learners, who usually struggle to find the right collocate …...

nan

Publication: TIPSTER (2016)

DOI: nan

Paper ID: 15855

AbstractWhile measuring the readability of texts has been a long-standing research topic, assessing the technicality of terms has only been addressed more recently and mostly for the English language. In this paper, we train a learning-to-rank model to determine a specialization degree for each term found in a given list. …...