Refine
Year of publication
Document Type
- Conference Proceeding (75)
- Part of a Book (29)
- Article (14)
- Book (6)
- Other (3)
- Doctoral Thesis (2)
- Working Paper (2)
- Image (1)
- Master's Thesis (1)
- Part of Periodical (1)
Language
- English (98)
- German (37)
- Multiple languages (1)
Has Fulltext
- yes (136)
Keywords
- Computerlinguistik (136) (remove)
Publicationstate
- Veröffentlichungsversion (136) (remove)
Reviewstate
Publisher
- Association for Computational Linguistics (20)
- European Language Resources Association (8)
- de Gruyter (8)
- LiU Electronic Press (6)
- Gesellschaft für Sprachtechnologie und Computerlinguistik (5)
- European Language Resources Association (ELRA) (4)
- Institut für Deutsche Sprache (4)
- CLARIN (3)
- Linköping University Electronic Press (3)
- Narr (3)
This thesis is a corpus linguistic investigation of the language used by young German speakers online, examining lexical, morphological, orthographic, and syntactic features and changes in language use over time. The study analyses the language in the Nottinghamer Korpus deutscher YouTube‐Sprache ("Nottingham corpus of German YouTube language", or NottDeuYTSch corpus), one of the first large corpora of German‐language comments taken from the videosharing website YouTube, and built specifically for this project. The metadatarich corpus comprises c.33 million tokens from more than 3 million comments posted underneath videos uploaded by mainstream German‐language youthorientated YouTube channels from 2008‐2018.
The NottDeuYTSch corpus was created to enable corpus linguistic approaches to studying digital German youth language (Jugendsprache), having identified the need for more specialised web corpora (see Barbaresi 2019). The methodology for compiling the corpus is described in detail in the thesis to facilitate future construction of web corpora. The thesis is situated at the intersection of Computer‐Mediated Communication (CMC) and youth language, which have been important areas of sociolinguistic scholarship since the 1980s, and explores what we can learn from a corpus‐driven, longitudinal approach to (online) youth language. To do so, the thesis uses corpus linguistic methods to analyse three main areas:
1. Lexical trends and the morphology of polysemous lexical items. For this purpose, the analysis focuses on geil, one of the most iconic and productive words in youth language, and presents a longitudinal analysis, demonstrating that usage of geil has decreased, and identifies lexical items that have emerged as potential replacements. Additionally, geil is used to analyse innovative morphological productiveness, demonstrating how different senses of geil are used as a base lexeme or affixoid in compounding and derivation.
2. Syntactic developments. The novel grammaticalization of several subordinating conjunctions into both coordinating conjunctions and discourse markers is examined. The investigation is supported by statistical analyses that demonstrate an increase in the use of non‐standard syntax over the timeframe of the corpus and compares the results with other corpora of written language.
3. Orthography and the metacommunicative features of digital writing. This analysis identifies orthographic features and strategies in the corpus, e.g. the repetition of certain emoji, and develops a holistic framework to study metacommunicative functions, such as the communication of illocutionary force, information structure, or the expression of identities. The framework unifies previous research that had focused on individual features, integrating a wide range of metacommunicative strategies within a single, robust system of analysis.
By using qualitative and computational analytical frameworks within corpus linguistic methods, the thesis identifies emergent linguistic features in digital youth language in German and sheds further light on lexical and morphosyntactic changes and trends in the language of young people over the period 2008‐2018. The study has also further developed and augmented existing analytical frameworks to widen the scope of their application to orthographic features associated with digital writing.
We present an implemented XML data model and a new, simplified query language for multi-level annotated corpora. The new query language involves automatic conversion of queries into the underlying, more complicated MMAXQL query language. It supports queries for sequential and hierarchical, but also associative (e.g. coreferential) relations. The simplified query language has been designed with non-expert users in mind.
So far, there have been few descriptions on creating structures capable of storing lexicographic data, ISO 24613:2008 being one of the latest. Another one is by Spohr (2012), who designs a multifunctional lexical resource which is able to store data of different types of dictionaries in a user-oriented way. Technically, his design is based on the principle of a hierarchical XML/OWL (eXtensible Markup Language/Web Ontology Language) representation model. This article follows another route in describing a model based on entities and relations between them; MySQL (usually referred to as: Structured Query Language) describes a database system of tables containing data and definitions of relations between them. The model was developed in the context of the project "Scientific eLexicography for Africa" and the lexicographic database to be built thereof will be implemented with MySQL. The principles of the ISO model and of Spohr's model are adhered to with one major difference in the implementation strategy: we do not place the lemma in the centre of attention, but the sense description — all other elements, including the lemma, depend on the sense description. This article also describes the contained lexicographic data sets and how they have been collected from different sources. As our aim is to compile several prototypical internet dictionaries (a monolingual Northern Sotho dictionary, a bilingual learners' Xhosa–English dictionary and a bilingual Zulu–English dictionary), we describe the necessary microstructural elements for each of them and which principles we adhere to when designing different ways of accessing them. We plan to make the model and the (empty) database with all graphical user interfaces that have been developed, freely available by mid-2015.
We present a gold standard for semantic relation extraction in the food domain for German. The relation types that we address are motivated by scenarios for which IT applications present a commercial potential, such as virtual customer advice in which a virtual agent assists a customer in a supermarket in finding those products that satisfy their needs best. Moreover, we focus on those relation types that can be extracted from natural language text corpora, ideally content from the internet, such as web forums, that are easy to retrieve. A typical relation type that meets these requirements are pairs of food items that are usually consumed together. Such a relation type could be used by a virtual agent to suggest additional products available in a shop that would potentially complement the items a customer has already in their shopping cart. Our gold standard comprises structural data, i.e. relation tables, which encode relation instances. These tables are vital in order to evaluate natural language processing systems that extract those relations.
Creating and maintaining metadata for various kinds of resources requires appropriate tools to assist the user. The paper presents the metadata editor ProFormA for the creation and editing of CMDI (Component Metadata Infrastructure) metadata in web forms. This editor supports a number of CMDI profiles currently being provided for different types of resources. Since the editor is based on XForms and server-side processing, users can create and modify CMDI files in their standard browser without the need for further processing. Large parts of ProFormA are implemented as web services in order to reuse them in other contexts and programs.
The understanding of story variation, whether motivated by cultural currents or other factors, is important for applications of formal models of narrative such as story generation or story retrieval. We present the first stage of an experiment to elicit natural narrative variation data suitable for evaluation with respect to story similarity, to qualitative and quantitative analysis of story variation, and also for data processing. We also present few preliminary results from the first stage of the experiment, using Red Riding Hood and Romeo and Juliet as base texts.
XML has been designed for creating structured documents, but the information that is encoded in these structures are, by definition, out of scope for XML. Additional sources, normally not easily interpretable by computers, such as documentation are needed to determine the intention of specific tags in a tag-set. The Component Metadata Infrastructure (CMDI) takes a rather pragmatic approach to foster interoperability between XML instances in the domain of metadata descriptions for language resources. This paper gives an overview of this approach.
Feedback utterances are among the most frequent in dialogue. Feedback is also a crucial aspect of linguistic theories that take social interaction, involving language, into account. This paper introduces the corpora and datasets of a project scrutinizing this kind of feedback utterances in French. We present the genesis of the corpora (for a total of about 16 hours of transcribed and phone force-aligned speech) involved in the project. We introduce the resulting datasets and discuss how they are being used in on-going work with focus on the form-function relationship of conversational feedback. All the corpora created and the datasets produced in the framework of this project will be made available for research purposes.
This paper presents a survey on hate speech detection. Given the steadily growing body of social media content, the amount of online hate speech is also increasing. Due to the massive scale of the web, methods that automatically detect hate speech are required. Our survey describes key areas that have been explored to automatically recognize these types of utterances using natural language processing. We also discuss limits of those approaches.
This paper presents a survey on the role of negation in sentiment analysis. Negation is a very common linguistic construction that affects polarity and, therefore, needs to be taken into consideration in sentiment analysis.
We will present various computational approaches modeling negation in sentiment analysis. We will, in particular, focus on aspects such as level of representation used for sentiment analysis, negation word detection and scope of negation. We will also discuss limits and challenges of negation modeling on that task.
Dieser Beitrag versucht, eine Einschätzung der Einsatzmöglichkeiten für automatische Analysemethoden aus der aktuellen computerlinguistischen Forschung für die sprachvergleichende Grammatikforschung vorzunehmen. Zur Illustration werden die Ergebnisse einer computerlinguistischen Studie für die vergleichende Untersuchung von Spaltsatzkonstruktionen in verschiedenen Sprachen wiedergegeben und ausführlich diskutiert. Der Korpuszugang erfolgt in diesem Rahmen auf Basis einer vollautomatischen syntaktischen Analyse, die dann noch zusätzlich durch eine statistische Wortalignierung kontrastiv auf Parallelkorpora beleuchtet werden kann. Neben der Vorstellung der bereits bestehenden automatischen Annotationsmöglichkeiten, die in meinen Augen vielversprechende Wege für den sprachwissenschaftlichen Korpuszugang eröffnen, ist die Hoffnung, dass dieser Beitrag durch die abschließende Diskussion zu dem Bewusstsein beiträgt, dass eine tiefere, organischere Verbindung der beiden sprachwissenschaftlichen Disziplinen möglich ist: dann nämlich, wenn der Korpuszugang nicht mit statischen, vordefinierten Werkzeugen erfolgt, deren Verhalten durch die Grammatikforscherin oder den Grammatikforscher nicht beeinflusst werden kann, sondern wenn ein interaktiver Werkzeuggebrauch erfolgt, der von den vielfältigen Anpassungsmöglichkeiten mit den zugrunde liegenden maschinellen Lernverfahren Gebrauch macht.
In this paper, we investigate the practical applicability of Co-Training for the task of building a classifier for reference resolution. We are concerned with the question if Co-Training can significantly reduce the amount of manual labeling work and still produce a classifier with an acceptable performance.
We use a convolutional neural network to perform authorship identification on a very homogeneous dataset of scientific publications. In order to investigate the effect of domain biases, we obscure words below a certain frequency threshold, retaining only their POS-tags. This procedure improves test performance due to better generalization on unseen data. Using our method, we are able to predict the authors of scientific publications in the same discipline at levels well above chance.
We present an implemented machine learning system for the automatic detection of nonreferential it in spoken dialog. The system builds on shallow features extracted from dialog transcripts. Our experiments indicate a level of performance that makes the system usable as a preprocessing filter for a coreference resolution system. We also report results of an annotation study dealing with the classification of it by naive subjects.
Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction
(2014)
We present a weakly-supervised induction method to assign semantic information to food items. We consider two tasks of categorizations being food-type classification and the distinction of whether a food item is composite or not. The categorizations are induced by a graph-based algorithm applied on a large unlabeled domain-specific corpus. We show that the usage of a domain-specific corpus is vital. We do not only outperform a manually designed open-domain ontology but also prove the usefulness of these categorizations in relation extraction, outperforming state-of-the-art features that include syntactic information and Brown clustering.
Bootstrapping Supervised Machine-learning Polarity Classifiers with Rule-based Classification
(2010)
In this paper, we explore the effectiveness of bootstrapping supervised machine-learning polarity classifiers using the output of domain-independent rule-based classifiers. The benefit of this method is that no labeled training data are required. Still, this method allows to capture in-domain knowledge by training the supervised classifier on in-domain features, such as bag of words.
We investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. The former addresses the issue in how far relevant constructions for polarity classification, such as word sense disambiguation, negation modeling, or intensification, are important for this self-training approach. We not only compare how this method relates to conventional semi-supervised learning but also examine how it performs under more difficult settings in which classes are not balanced and mixed reviews are included in the dataset.
Active learning has been applied to different NLP tasks, with the aim of limiting the amount of time and cost for human annotation. Most studies on active learning have only simulated the annotation scenario, using prelabelled gold standard data. We present the first active learning experiment for Word Sense Disambiguation with human annotators in a realistic environment, using fine-grained sense distinctions, and investigate whether AL can reduce annotation cost and boost classifier performance when applied to a real-world task.
German is a language with complex morphological processes. Its long and often ambiguous word forms present a bottleneck problem in natural language processing. As a step towards morphological analyses of high quality, this paper introduces a morphological treebank for German. It is derived from the linguistic database CELEX which is a standard resource for German morphology. We build on its refurbished, modernized and partially revised version. The derivation of the morphological trees is not trivial, especially for such cases of conversions which are morpho-semantically opaque and merely of diachronic interest. We develop solutions and present exemplary analyses. The resulting database comprises about 40,000 morphological trees of a German base vocabulary whose format and grade of detail can be chosen according to the requirements of the applications. The Perl scripts for the generation of the treebank are publicly available on github. In our discussion, we show some future directions for morphological treebanks. In particular, we aim at the combination with other reliable lexical resources such as GermaNet.
Catching the common cause: extraction and annotation of causal relations and their participants
(2017)
In this paper, we present a simple, yet effective method for the automatic identification and extraction of causal relations from text, based on a large English-German parallel corpus. The goal of this effort is to create a lexical resource for German causal relations. The resource will consist of a lexicon that describes constructions that trigger causality as well as the participants of the causal event, and will be augmented by a corpus with annotated instances for each entry, that can be used as training data to develop a system for automatic classification of causal relations. Focusing on verbs, our method harvested a set of 100 different lexical triggers of causality, including support verb constructions. At the moment, our corpus includes over 1,000 annotated instances. The lexicon and the annotated data will be made available to the research community.
CLARIAH-DE cross-service search - prospects and benefits of merging subject-specific services
(2021)
CLARIAH-DE combines services and offerings of CLARIN-D and DARIAH-DE. This includes various search applications which are made directly available to researchers. These search applications are presented in this working paper based on their main characteristics and compared with a focus on possible harmonizations. Opportunities and risks of different forms of technical integration are highlighted. Identified challenges can be explained in particular considering the background of different organizational and technical frameworks as well as highly specific and discipline-dependent requirements. The integration work that has already been carried out and the experiences gained with regard to future work and possible integration of further applications are also discussed. The experiences made in CLARIAH-DE can especially be of interest for other projects in the field of digital research infrastructures.
Data Management is one of the core activities of all CLARIN centres providing data and services for the academia. In PARTHENOS, European initiatives and projects in the area of the humanities and social sciences assembled to compare policies and procedures. One of the areas of interest is data management. The data management landscape shows a lot of proliferation, for which an abstraction level is introduced to help centres, such as CLARIN centres, in the process of providing the best possible services to users with data management needs.
We discovered several recurring errors in the current version of the Europarl Corpus originating both from the web site of the European Parliament and the corpus compilation based thereon. The most frequent error was incompletely extracted metadata leaving non-textual fragments within the textual parts of the corpus files. This is, on average, the case for every second speaker change. We not only cleaned the Europarl Corpus by correcting several kinds of errors, but also aligned the speakers’ contributions of all available languages and compiled every- thing into a new XML-structured corpus. This facilitates a more sophisticated selection of data, e.g. querying the corpus for speeches by speakers of a particular political group or in particular language combinations.
In this Paper, we describe a schema and models which have been developed for the representation of corpora of computer-mediated communicatin (CMC corpora) using the representation framework provided by the Text Encoding Initiative (TEI). We characterise CMC discourse as dialogic, sequentially organised interchange between humans and point out that many features of CMC are not adequately handled by current corpus encoding schemas and tools. We formulate desiderata for a representation of CMC in encoding schemes and argue why the TEI is a suitable framework for the encoding of CMC corpora. We propose a model of basic CMC units (utterances, posts, and nonverbal activities) and the macro- and micro-level structures of interactions in CMC environments. Based on these models, we introduce CMC-core, a TEI customisation for the encoding of CMC corpora, which defines CMC-specific encoding features on the four levels of elements, model classes, attribute classes, and modules of the TEI infrastructure. The description of our customisation is illustrated by encoding examples from corpora by researchers of the TEI SIG CMC, representing a variety of CMC genres, i.e. chat, wiki talk, twitter, blog, and Second Life interactions. The material described, i.e. schemata, encoding examples, and documentation, is available from the of the TEI CMC SIG Wiki and will accompany a feature request to the TEI council in late 2019.
The paper’s purpose is to give an overview of the work on the Component Metadata Infrastructure (CMDI) that was implemented in the CLARIN research infrastructure. It explains, the underlying schema, the accompanying tools and services. It also describes the status and impact of the CMDI developments done within the CLARIN project and past and future collaborations with other projects.
Learning new languages has a high relevance in today’s society with a globalized economy and the freedom to move abroad for work, study or other reasons. In this context new methods to teach and learn languages with the help of modern technology are becoming more relevant besides traditional language classes.
This work presents a new approach to combine a traditional language class with a modern computer-based approach for teaching. As a concrete example a web application to help teach and learn Latin was developed.
Constructing a Corpus
(2016)
Opinion holder extraction is one of the important subtasks in sentiment analysis. The effective detection of an opinion holder depends on the consideration of various cues on various levels of representation, though they are hard to formulate explicitly as features. In this work, we propose to use convolution kernels for that task which identify meaningful fragments of sequences or trees by themselves. We not only investigate how different levels of information can be effectively combined in different kernels but also examine how the scope of these kernels should be chosen. In general relation extraction, the two candidate entities thought to be involved in a relation are commonly chosen to be the boundaries of sequences and trees. The definition of boundaries in opinion holder extraction, however, is less straightforward since there might be several expressions beside the candidate opinion holder to be eligible for being a boundary.
In this paper, we explore different linguistic structures encoded as convolution kernels for the detection of subjective expressions. The advantage of convolution kernels is that complex structures can be directly provided to a classifier without deriving explicit features. The feature design for the detection of subjective expressions is fairly difficult and there currently exists no commonly accepted feature set. We consider various structures, such as constituency parse structures, dependency parse structures, and predicate-argument structures. In order to generalize from lexical information, we additionally augment these structures with clustering information and the task-specific knowledge of subjective words. The convolution kernels will be compared with a standard vector kernel.
In this paper, we examine methods to automatically extract domain-specific knowledge from the food domain from unlabeled natural language text. We employ different extraction methods ranging from surface patterns to co-occurrence measures applied on different parts of a document. We show that the effectiveness of a particular method depends very much on the relation type considered and that there is no single method that works equally well for every relation type. We also examine a combination of extraction methods and also consider relationships between different relation types. The extraction methods are applied both on a domain-specific corpus and the domain-independent factual knowledge base Wikipedia. Moreover, we examine an open-domain lexical ontology for suitability.
Datenmanagement wird durch die Forschungsföderungsorganisationen (etwa in Horizon 2020 der EU, die Allianz der deutschen Wissenschaftsorganisationen oder in DFG geförderten Projekten) mehr und mehr Teil der Forschungslandschaft. Für die Computerlinguistik ist das Forschungsdatenmanagement aber auch Teil des Forschungsgebietes: Datenmodellierung und Transformation für die nachhaltige Datenspeicherung gehören in den Bereich der Texttechnologie und Textlinguistik, ebenso die Modellierung der beschreibenden Daten zu Datensätzen.
We present a language learning application that relies on grammars to model the learning outcome. Based on this concept we can provide a powerful framework for language learning exercises with an intuitive user interface and a high reliability. Currently the application aims to augment existing language classes and support students by improving the learner attitude and the general learning outcome. Extensions beyond that scope are promising and likely to be added in the future.
Im Teilprojekt CI “SemDok” der DFG-Forschergruppe Texttechnologische Informationsmodellierung wurde ein Textparser für Diskursstrukturen wissenschaftlicher Zeitschriftenartikel nach der Rhetorical Structure Theory entwickelt. Die wesentlichen konzeptuellen und technischen Merkmale des Chart-Parsers und die sich daraus ergebenden Parametrisierungsmöglichkeiten für Parsing-Experimente werden beschrieben. Zudem wird HPVtz., ein Tool für die Visualisierung von Parsing-Ergebnissen (RST-Bäume in einer XML-Anwendung) und die Navigation in ihnen, vorgestellt.
In 1959, Lucien Tesnière wrote his main work Éléments de syntaxe structurale. While the impact on theoretical linguistics was not very strong at first, 50 years later there exist a variety of linguistic theories based on Tesnière's work. In computational linguistics, as in theoretical linguistics, dependency grammar was not very influential at first. The last 10–15 years, however, have brought a noticeable change and dependency grammar has found its way into computational linguistics. Syntactically annotated corpora based on dependency representations are available for a variety of languages, as well as statistical parsers which give a syntactic analysis of running text describing the underlying dependency relations between word tokens in the text. This article gives an overview of relevant areas of computational linguistics which have been influenced by dependency grammar. It discusses the pros and cons of different types of syntactic representation used in natural language processing and their suitability as representations of meaning. Finally, an attempt is made to give an outlook on the future impact of dependency grammar on computational linguistics.
Der Einsatz einer maßgeschneiderten, feingranularen XML-Modellierung im lexikografischen Prozess
(2011)
In this chapter, we discuss steps toward extending CMDI’s semantic interoperability beyond the Social Sciences and Humanities: We stress the need for an initial data curation step, in part supported by a relation registry that helps impose some structure on CMDI vocabulary; we describe the use of authority file information and other controlled vocabulary to help connecting CMDI-based metadata to existing Linked Data; we show how significant parts of CMDI-based metadata can be converted to bibliographic metadata standards and hence entered into library catalogs; and finally we describe first steps to convert CMDI-based metadata to RDF. The initial grassroots approach of CMDI (meaning that anybody can define metadata descriptors and components) mirrors the AAA slogan of the Semantic Web (“Anyone can say Anything about Any topic”). Ironically, this makes it hard to fully link CMDI-based metadata to other Semantic Web datasets. This paper discusses the challenges of this enterprise.
We introduce a method for error detection in automatically annotated text, aimed at supporting the creation of high-quality language resources at affordable cost. Our method combines an unsupervised generative model with human supervision from active learning. We test our approach on in-domain and out-of-domain data in two languages, in AL simulations and in a real world setting. For all settings, the results show that our method is able to detect annotation errors with high precision and high recall.
Die Darstellbarkeit lexikalischen Wissens – am Beispiel kommunikativer Ausdrücke des Deutschen
(1995)
Editorial
(2020)
Linguistische Studien arbeiten häufig mit einer Differenzierung zwischen gesprochener und geschriebener Sprache bzw. zwischen Kommunikation der Nähe und Distanz. Die Annahme eines Kontinuums zwischen diesen Polen bietet sich für eine Verortung unterschiedlichster Äußerungsformen an, inklusive unkonventioneller Textsorten wie etwa Popsongs. Wir konzipieren, implementieren und evaluieren ein automatisiertes Verfahren, das mithilfe unkorrelierter Entscheidungsbäume entsprechende Vorhersagen auf Textebene durchführt. Für die Identifizierung der Pole definieren wir einen Merkmalskatalog aus Sprachphänomenen, die als Markierer für Nähe/Mündlichkeit bzw. Distanz/Schriftlichkeit diskutiert werden, und wenden diesen auf prototypische Nähe-/Mündlichkeitstexte sowie prototypische Distanz-/Schrifttexte an. Basierend auf der sehr guten Klassifikationsgüte verorten wir anschließend eine Reihe weiterer Textsorten mithilfe der trainierten Klassifikatoren. Dabei erscheinen Popsongs als „mittige Textsorte“, die linguistisch motivierte Merkmale unterschiedlicher Kontinuumsstufen vereint. Weiterhin weisen wir nach, dass unsere Modelle mündlich kommunizierte, aber vorab oder nachträglich verschriftlichte Äußerungen wie Reden oder Interviews vollkommen anders verorten als prototypische Gesprächsdaten und decken Klassifikationsunterschiede für Social-Media-Varianten auf. Ziel ist dabei nicht eine systematisch-verbindliche Einordung im Kontinuum, sondern eine empirische Annäherung an die Frage, welche maschinell vergleichsweise einfach bestimmbaren Merkmale („shallow features“) nachweisbar Einfluss auf die Verortung haben.
The Data Governance Act was proposed in late 2020 as part of the European Strategy for Data, and adopted on 30 May 2022 (as Regulation 2022/868). It will enter into application on 24 September 2023. The Data governance Act is a major development in the legal framework affecting CLARIN and the whole language community. With its new rules on the re-use of data held by the public sector bodies and on the provision of data sharing services, and especially its encouragement of data altruism, the Data Governance Act creates new opportunities and new challenges for CLARIN ERIC. This paper analyses the provisions of the Data Governance Act, and aims at initiating the debate on how they will impact CLARIN and the whole language community.
We evaluate a graph-based dependency parser on DeReKo, a large corpus of contemporary German. The dependency parser is trained on the German dataset from the SPMRL 2014 Shared Task which contains text from the news domain, whereas DeReKo also covers other domains including fiction, science, and technology. To avoid the need for costly manual annotation of the corpus, we use the parser’s probability estimates for unlabeled and labeled attachment as main evaluation criterion. We show that these probability estimates are highly correlated with the actual attachment scores on a manually annotated test set. On this basis, we compare estimated parsing scores for the individual domains in DeReKo, and show that the scores decrease with increasing distance of a domain to the training corpus.
Unknown words are a challenge for any NLP task, including sentiment analysis. Here, we evaluate the extent to which sentiment polarity of complex words can be predicted based on their morphological make-up. We do this on German as it has very productive processes of derivation and compounding and many German hapax words, which are likely to bear sentiment, are morphologically complex. We present results of supervised classification experiments on new datasets with morphological parses and polarity annotations.