Automatic recognition of speech, thought, and writing representation in German narrative texts

Brunner, Annelen

doi:10.1093/llc/fqt024

Cited by 12 publications

(14 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Models. Brunner (2013) proposes two models for quotation annotation on RWG. Both models work at the sentence level and predict only the presence of absence of quotations, not their spans (even though this information is annotated).…”

Section: Redewiedergabe Datasetmentioning

confidence: 99%

“…As is often the case with semantic phenomena, manual annotation of quotations has shown to be slow and resource-intensive, in particular when undertaken in conjunction with the annotation of speakers and information quality (Brunner, 2013;Pareti, 2015). This provides the rationale for automatic quotation recognition methods.…”

Section: Introductionmentioning

confidence: 99%

“…This provides the rationale for automatic quotation recognition methods. After a first round of rule-based methods (Pouliquen et al, 2007;Brunner, 2013), recent supervised models use mostly sequence classifiers (Pareti et al, 2013;Almeida et al, 2014;Scheible et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Quotation Detection and Classification with a Corpus-Agnostic Model

Papay¹,

Padó²

2019

Proceedings - Natural Language Processing in a Deep Learning World

View full text Add to dashboard Cite

The detection of quotations (i.e., reported speech, thought, and writing) has established itself as an NLP analysis task. However, state-of-the-art models have been developed on the basis of specific corpora and incorporate a high degree of corpus-specific assumptions and knowledge, which leads to fragmentation. In the spirit of task-agnostic modeling, we present a corpus-agnostic neural model for quotation detection and evaluate it on three corpora that vary in language, text genre, and structural assumptions. The model (a) approaches the state-of-the-art on the corpora when using established feature sets and (b) shows reasonable performance even when using solely word forms, which makes it applicable for non-standard (i.e., historical) corpora.

show abstract

Section: Redewiedergabe Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quotation Detection and Classification with a Corpus-Agnostic Model

Papay¹,

Padó²

2019

Proceedings - Natural Language Processing in a Deep Learning World

View full text Add to dashboard Cite

show abstract

“…Among these are, for example, proportions of text type (argumentative, narrative, descriptive), ratio of narrative vs. dialogue passages, or types of focalization. Recent work on automatically identifying reported speech (Brunner 2013) or descriptions (Jautze et al 2013) using rulebased as well as machinelearning techniques, are examples of such new avenues for quantitative stylistic research. Despite such advances, we contend with stylisticians such as Leech and Short (2007) that one cannot really get a hold on all formal features that make up the style of some unit, but that one has to select certain features: for practical reasons, but also because of methodological limitations.…”

Section: A New Definition Of Stylementioning

confidence: 99%

Revisiting Style, a Key Concept in Literary Studies

Herrmann¹,

Dalen-Oskam²,

Schöch³

2015

Journal of Literary Theory

View full text Add to dashboard Cite

Language and literary studies have studied style for centuries, and even since the advent of ›stylistics‹ as a discipline at the beginning of the twentieth century, definitions of ›style‹ have varied heavily across time, space and fields. Today, with increasingly large collections of literary texts being made available in digital form, computational approaches to literary style are proliferating. New methods from disciplines such as corpus linguistics and computer science are being adopted and adapted in interrelated fields such as computational stylistics and corpus stylistics, and are facilitating new approaches to literary style.The relation between definitions of style in established linguistic or literary stylistics, and definitions of style in computational or corpus stylistics has not, however, been systematically assessed. This contribution aims to respond to the need to redefine style in the light of this new situation and to establish a clearer perception of both the overlap and the boundaries between ›mainstream‹ and ›computational‹ and/or ›empirical‹ literary stylistics. While stylistic studies of non-literary texts are currently flourishing, our contribution deliberately centers on those approaches relevant to ›literary stylistics‹. It concludes by proposing an operational definition of style that we hope can act as a common ground for diverse approaches to literary style, fostering transdisciplinary research.The focus of this contribution is on literary style in linguistics and literary studies (rather than in art history, musicology or fashion), on textual aspects of style (rather than production-or reception-oriented theories of style), and on a descriptive perspective (rather than a prescriptive or didactic one). Even within these limits, however, it appears necessary to build on a broad understanding of the various perspectives on style that have been adopted at different times and in different traditions. For this reason, the contribution first traces the development of the notion of style in three different traditions, those of German, Dutch and French language and literary studies. Despite the numerous links between each other, and between each of them to the British and American traditions, these three traditions each have their proper dynamics, especially with regard to the convergence and/or confrontation between mainstream and computational stylistics. For reasons of space and coherence, the contribution is limited to theoretical developments occurring since 1945.The contribution begins by briefly outlining the range of definitions of style that can be encountered across traditions today: style as revealing a higherorder aesthetic value, as the holistic ›gestalt‹ of single texts, as an expression of the individuality of an author, as an artifact presupposing choice among alternatives, as a deviation from a norm or reference, or as any formal property of a text. The contribution then traces the development of definitions of style in each of the three traditions mentioned, with t...

show abstract

“…For example, the lists of connectives, causal and temporal clause detection were replaced by German equivalents. The detection of direct, indirect, and reported speech was done following Brunner (2013). Further, corpus similarity was computed based on the Tiger corpus (Brants et al, 2004) instead of the Brown corpus, and the word frequency was calculated using the German part of Web1T.…”

Section: Essay Grading Systemmentioning

confidence: 99%

Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications

Tetreault

Burstein

Leacock³

2015

View full text Add to dashboard Cite

iii IntroductionWe are excited to be holding the 10th anniversary the BEA workshop. Since starting in 1997, the BEA workshop, now one of the largest workshops at NAACL/ACL, has become one of the leading venues for publishing innovative work that uses NLP to develop educational applications. The consistent interest in and growth of the workshop has clear ties to societal need and related advances in the technology, and the maturity of the NLP/education field. NLP capabilities now support an array of learning domains, including writing, speaking, reading, and mathematics. Within these domains, the community continues to develop and deploy innovative NLP approaches for use in educational settings. In the writing and speech domains, automated writing evaluation (AWE) and speech scoring applications, respectively, are commercially deployed in high-stakes assessment and instructional settings, including Massive Open Online Courses (MOOCs). We also see widely-used commercial applications for plagiarism detection and peer review. Major advances in speech technology, have made it possible to include speech in both assessment and Intelligent Tutoring Systems. There has been a renewed interest in spoken dialog and multi-modal systems for instruction and assessment as well as feedback. We are also seeing explosive growth of mobile applications for game-based applications for instruction and assessment. The current educational and assessment landscape, continues to foster a strong interest and high demand that pushes the state-of-the-art in AWE capabilities to expand the analysis of written responses to writing genres other than those traditionally found in standardized assessments, especially writing tasks requiring use of sources and argumentative discourse.The use of NLP in educational applications has gained visibility outside of the NLP community. First, the Hewlett Foundation reached out to public and private sectors and sponsored two competitions: one for automated essay scoring, and the other for scoring of short answer, fact-based response items. The motivation driving these competitions was to engage the larger scientific community in this enterprise. MOOCs are now beginning to incorporate AWE systems to manage the thousands of constructedresponse assignments collected during a single MOOC course. Learning@Scale is a recent venue for discussing NLP research in education. The NLP-TEA workshop, now in its second year (NLP-TEA2), gives special attention to papers working on Asian languages. The Speech and Language Technology in Education (SLaTE), now in its sixth year, promotes the use of speech and language technology for educational purposes. Another breakthrough for educational applications within the CL community is the presence of a number of shared-task competitions over the last three years. There have been three shared tasks on grammatical error correction with the most recent edition hosted at CoNLL 2014. In 2014 alone, there were four shared tasks for NLP and Education-related areas.As a community, we contin...

show abstract

Automatic recognition of speech, thought, and writing representation in German narrative texts

Cited by 12 publications

References 8 publications

Quotation Detection and Classification with a Corpus-Agnostic Model

Quotation Detection and Classification with a Corpus-Agnostic Model

Revisiting Style, a Key Concept in Literary Studies

Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications

Contact Info

Product

Resources

About