Refine
Year of publication
Document Type
- Conference Proceeding (62)
- Part of a Book (19)
- Article (13)
- Working Paper (2)
- Book (1)
- Doctoral Thesis (1)
Keywords
- Automatische Sprachanalyse (29)
- Deutsch (20)
- Korpus <Linguistik> (17)
- Computerlinguistik (16)
- Frame-Semantik (15)
- Annotation (14)
- Semantische Analyse (14)
- Propositionale Einstellung (10)
- Beleidigung (9)
- sentiment analysis (9)
Publicationstate
- Veröffentlichungsversion (75)
- Zweitveröffentlichung (8)
- Postprint (5)
Reviewstate
Publisher
- Association for Computational Linguistics (13)
- European Language Resources Association (10)
- The Association for Computational Linguistics (9)
- German Society for Computational Linguistics & Language Technology und Friedrich-Alexander-Universität Erlangen-Nürnberg (4)
- Springer (4)
- European Language Resources Association (ELRA) (3)
- European language resources association (ELRA) (3)
- Oxford University Press (3)
- Universität Hildesheim (3)
- EACL (2)
Authors like Fillmore 1986 and Goldberg 2006 have made a strong case for regarding argument omission in English as a lexical and construction-based affordance rather than one based on general semantico-pragmatic constraints. They do not, however, address the question of how grammatical restrictions on null complementation might interact with broader narrative conventions, in particular those of genre. In this paper, we attempt to remedy this oversight by presenting a comprehensive overview of genre-based argument omissions and offering a construction-based analysis of genre-based omission conventions. We consider five genre-based omission types: instructional imperatives (Culy 1996, Bender 1999), labelese, diary style (Haegeman 1990), match reports (Ruppenhofer 2004) and quotative clauses. We show that these omission types share important traits; all, for example, have anaphoric rather than indefinite construals. We also show, however, that the omission types differ from each other in idiosyncratic ways. We then address several interrelated representational problems posed by the grammatical treatment of genre-based omissions. For example, the constructions that represent genre-based omission conventions must interact with the lexical entries of verbs, many of which do not generally permit omitted arguments. Accordingly, we offer constructional analyses of genre-based omissions that allow constructions to override lexical valence constraints.
We present a descriptive analysis on the two datasets from the shared task on Source, Subjective Expression and Target Extraction from Political Speeches (STEPS), the only existing German dataset for opinion role extraction of its size. Our analysis discusses the individual properties of the three components, subjective expressions, sources and targets and their relations towards each other. Our observations should help practitioners and researchers when building a system to extract opinion roles from German data.
We present a testsuite for POS tagging German web data. Our testsuite provides the original raw text as well as the gold tokenisations and is annotated for parts-of-speech. The testsuite includes a new dataset for German tweets, with a current size of 3,940 tokens. To increase the size of the data, we harmonised the annotations in already existing web corpora, based on the Stuttgart-Tübingen Tag Set. The current version of the corpus has an overall size of 48,344 tokens of web data, around half of it from Twitter. We also present experiments, showing how different experimental setups (training set size, additional out-of-domain training data, self-training) influence the accuracy of the taggers. All resources and models will be made publicly available to the research community.
We present a new resource for German causal language, with annotations in context for verbs, nouns and adpositions. Our dataset includes 4,390 annotated instances for more than 150 different triggers. The annotation scheme distinguishes three different types of causal events (CONSEQUENCE, MOTIVATION, PURPOSE). We also provide annotations for semantic roles, i.e. of the cause and effect for the causal event as well as the actor and affected party, if present. In the paper, we present inter-annotator agreement scores for our dataset and discuss problems for annotating causal language. Finally, we present experiments where we frame causal annotation as a sequence labelling problem and report baseline results for the prediciton of causal arguments and for predicting different types of causation.
A Supervised learning approach for the extraction of opinion sources and targets from German text
(2019)
We present the first systematic supervised learning approach for the extraction of opinion sources and targets on German language data. A wide choice of different features is presented, particularly syntactic features and generalization features. We point out specific differences between opinion sources and targets. Moreover, we explain why implicit sources can be extracted even with fairly generic features. In order to ensure comparability our classifier is trained and tested on the dataset of the STEPS shared task.
This paper presents Release 2.0 of the SALSA corpus, a German resource for lexical semantics. The new corpus release provides new annotations for German nouns, complementing the existing annotations of German verbs in Release 1.0. The corpus now includes around 24,000 sentences with more than 36,000 annotated instances. It was designed with an eye towards NLP applications such as semantic role labeling but will also be a useful resource for linguistic studies in lexical semantics.
Auf dem Weg zu einer Kartographie: automatische und manuelle Analysen am Beispiel des Korpus ISW
(2021)
Alleviating pain is good and abandoning hope is bad. We instinctively understand how words like alleviate and abandon affect the polarity of a phrase, inverting or weakening it. When these words are content words, such as verbs, nouns, and adjectives, we refer to them as polarity shifters. Shifters are a frequent occurrence in human language and an important part of successfully modeling negation in sentiment analysis; yet research on negation modeling has focused almost exclusively on a small handful of closed-class negation words, such as not, no, and without. A major reason for this is that shifters are far more lexically diverse than negation words, but no resources exist to help identify them. We seek to remedy this lack of shifter resources by introducing a large lexicon of polarity shifters that covers English verbs, nouns, and adjectives. Creating the lexicon entirely by hand would be prohibitively expensive. Instead, we develop a bootstrapping approach that combines automatic classification with human verification to ensure the high quality of our lexicon while reducing annotation costs by over 70%. Our approach leverages a number of linguistic insights; while some features are based on textual patterns, others use semantic resources or syntactic relatedness. The created lexicon is evaluated both on a polarity shifter gold standard and on a polarity classification task.
In this paper we use methods for creating a large lexicon of verbal polarity shifters and apply them to German. Polarity shifters are content words that can move the polarity of a phrase towards its opposite, such as the verb “abandon” in “abandon all hope”. This is similar to how negation words like “not” can influence polarity. Both shifters and negation are required for high precision sentiment analysis. Lists of negation words are available for many languages, but the only language for which a sizable lexicon of verbal polarity shifters exists is English. This lexicon was created by bootstrapping a sample of annotated verbs with a supervised classifier that uses a set of data- and resource-driven features. We reproduce and adapt this approach to create a German lexicon of verbal polarity shifters. Thereby, we confirm that the approach works for multiple languages. We further improve classification by leveraging cross-lingual information from the English shifter lexicon. Using this improved approach, we bootstrap a large number of German verbal polarity shifters, reducing the annotation effort drastically. The resulting German lexicon of verbal polarity shifters is made publicly available.
Alternations play a central role in most current theories of verbal argument structure, wich are devides primarily to model the syntactic flexibility of verbs. Accordingly, these frameworks take verbs, and their projection properties, to be the sole contributors of thematic content to the clause. Approached from this perspective, the German applicative (or be-prefix) construction has puzzling properties. First, while many applicative verbs have transparent base forms, many, including those coined from nouns, do not. Second, applicative verbs are bound by interpretive and argument-realization conditions which cannot be traced to their base forms, if any. These facts suggest that applicative formation is not appropriately modeled as a lexical rule.
Using corpus data from a diverse array of genres, Michaelis and Ruppenhofer propose a unified solution to these two puzzles within the framework of Construction Grammar. Central to this account is the concept of valence augmentation: argument-structure constructions denote event types, and therefore license valence sets which may properly include those of their lexical fillers. As per Panini's Law, resolution of valence mismatch favors the construction over the verb. Like verbs of transfer and location, the applicative construction has a prototype-based event-structure representation: diverse implications of applicative predications--including iteration, transfer, affectedness, intensity and saturation--are shown to derive via regular patterns of semantic extension from the topological concept of coverage.
Semantic role labeling is traditionally viewed as a sentence-level task concerned with identifying semantic arguments that are overtly realized in a fairly local context (i.e., a clause or sentence). However, this local view potentially misses important information that can only be recovered if local argument structures are linked across sentence boundaries. One important link concerns semantic arguments that remain locally unrealized (null instantiations) but can be inferred from the context. In this paper, we report on the SemEval 2010 Task-10 on ‘‘Linking Events and Their Participants in Discourse’’, that addressed this problem. We discuss the corpus that was created for this task, which contains annotations on multiple levels: predicate argument structure (FrameNet and PropBank), null instantiations, and coreference. We also provide an analysis of the task and its difficulties.
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.
The FrameNet lexical database yields information about collocations and multiword expressions in various ways. In some cases phrasal units have been entered from the start as lexical entries (write down). In other cases headword + preposition pairs can be recognized as special collocations Where the preposition in question is a necessary and lexically specified marker of an argument of the headword + fond of, hostile to). Nominal compounds are annotated with respect to noun or (pertinative) adjective modifiers, some of which are analyzable but also entrenched (wheel chair, fiscal year). Nouns that name aggregates, portions, types, etc., sometimes hold lexically specified relations to their dependents (flock of geese). And event nouns frequently Select the support verbs which permit them to enter into predications (file an objection, enter a plea). A subproject aims at extracting, as structured clusters of lexical items, the minimal semantically central kernel dependency graphs from the set of annotations. Such research will yield not only commonplace groupings (eat: dog, bone) but will also yield hitherto unnoticed collocations within such graphs (answer: you, door) where certain dependency links within them are idiomatic or otherwise lexically special, here answer > door. Collocational information can also be retrieved by various types of queries within our MySQL search tool
We compare several different corpus- based and lexicon-based methods for the scalar ordering of adjectives. Among them, we examine for the first time a low- resource approach based on distinctive- collexeme analysis that just requires a small predefined set of adverbial modifiers. While previous work on adjective intensity mostly assumes one single scale for all adjectives, we group adjectives into different scales which is more faithful to human perception. We also apply the methods to both polar and non-polar adjectives, showing that not all methods are equally suitable for both types of adjectives.
In this paper, we report on an effort to develop a gold standard for the intensity ordering of subjective adjectives. Rather than pursue a complete order as produced by paying attention to the mean scores of human ratings only, we take into account to what extent assessors consistently rate pairs of adjectives relative to each other. We show that different available automatic methods for producing polar intensity scores produce results that correlate well with our gold standard, and discuss some conceptual questions surrounding the notion of polar intensity.