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This study examines what kind of cues and constraints for discourse interpretation can be derived from the logical and generic document structure of complex texts by the example of scientific journal articles. We performed statistical analysis on a corpus of scientific articles annotated on different annotations layers within the framework of XML-based multi-layer annotation. We introduce different discourse segment types that constrain the textual domains in which to identify rhetorical relation spans, and we show how a canonical sequence of text type structure categories is derived from the corpus annotations. Finally, we demonstrate how and which text type structure categories assigned to complex discourse segments of the type “block” statistically constrain the occurrence of rhetorical relation types.
We introduce our pipeline to integrate CMC and SM corpora into the CLARIN-D corpus infrastructure. The pipeline was developed by transforming an existing CMC corpus, the Dortmund Chat Corpus, into a resource conforming to current technical and legal standards. We describe how the resource has been prepared and restructured in terms of TEI encoding, linguistic annotations, and anonymisation. The output is a CLARIN-conformant resource integrated in the CLARIN-D research infrastructure.
Opinion Holder and Target Extraction for Verb-based Opinion Predicates – The Problem is Not Solved
(2015)
We offer a critical review of the current state of opinion role extraction involving opinion verbs. We argue that neither the currently available lexical resources nor the manually annotated text corpora are sufficient to appropriately study this task. We introduce a new corpus focusing on opinion roles of opinion verbs from the Subjectivity Lexicon and show potential benefits of this corpus. We also demonstrate that state-of-the-art classifiers perform rather poorly on this new dataset compared to the standard dataset for the task showing that there still remains significant research to be done.
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.
Accurate opinion mining requires the exact identification of the source and target of an opinion. To evaluate diverse tools, the research community relies on the existence of a gold standard corpus covering this need. Since such a corpus is currently not available for German, the Interest Group on German Sentiment Analysis decided to create such a resource and make it available to the research community in the context of a shared task. In this paper, we describe the selection of textual sources, development of annotation guidelines, and first evaluation results in the creation of a gold standard corpus for the German language.
This paper presents ongoing research which is embedded in an empirical-linguistic research program, set out to devise viable research strategies for developing an explanatory theory of grammar as a psychological and social phenomenon. As this phenomenon cannot be studied directly, the program attempts to approach it indirectly through its correlates in language corpora, which is justified by referring to the core tenets of Emergent Grammar. The guiding principle for identifying such corpus correlates of grammatical regularities is to imitate the psychological processes underlying the emergent nature of these regularities. While previous work in this program focused on syntagmatic structures, the current paper goes one step further by investigating schematic structures that involve paradigmatic variation. It introduces and explores a general strategy by which corpus correlates of such structures may be uncovered, and it further outlines how these correlates may be used to study the nature of the psychologically real schematic structures.
The central issue in corpus-driven linguistics is the detection and description of patterns in language usage. The features that constitute the notion of a pattern can be computed to a certain extent by statistical (collocation) methods, but a crucial part of the notion may vary depending on applications and users. Thus, typically, any computed collocation cluster will have to be interpreted hermeneutically. Often it might be captured by a generalized, more abstract pattern. We present a generic process model that supports the recognition, interpretation, and expression of the patterns inside and of the relations between clusters. By this, clusters can be merged virtually according to any notion of a 'pattern', and their relations can be exploited for different applications
This introductory tutorial describes a strictly corpus-driven approach for uncovering indications for aspects of use of lexical items. These aspects include ‘(lexical) meaning’ in a very broad sense and involve different dimensions, they are established in and emerge from respective discourses. Using data-driven mathematical-statistical methods with minimal (linguistic) premises, a word’s usage spectrum is summarized as a collocation profile. Self-organizing methods are applied to visualize the complex similarity structure spanned by these profiles. These visualizations point to the typical aspects of a word’s use, and to the common and distinctive aspects of any two words.