Part-Of-Speech Tagging for Social Media Texts

Neunerdt, Melanie; Trevisan, Bianka; Reyer, Michael; Mathar, Rudolf

doi:10.1007/978-3-642-40722-2_15

Cited by 26 publications

(13 citation statements)

References 9 publications

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“…Beißwenger and Storrer, 2008;King, 2009; for the development of NLP tools e.g. Ritter et al, 2011;Gimpel et al, 2011;Owoputi et al, 2015;Avontuur et al, 2012;Bartz et al, 2013;Neunerdt et al, 2013;Rehbein, 2013;Horbach et al, 2015;Zinsmeister et al, 2014;Ljubešić et al, 2015). Issues of processing and annotating CMC data have also been a central topic of the DFG-funded scientific network Empirical Research of Internet-Based Communication (Empirikom), which brought together researchers interested in building and analyzing CMC, social media and Web corpora for research questions in linguistics, computational linguistics and language technology during the years 2010-2014.…”

Section: Motivation Premises and Goalsmentioning

confidence: 99%

EmpiriST 2015: A Shared Task on the Automatic Linguistic Annotation of Computer-Mediated Communication and Web Corpora

Beißwenger

Bartsch

Evert

et al. 2016

Proceedings of the 10th Web as Corpus Workshop

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This paper describes the goals, design and results of a shared task on the automatic linguistic annotation of German language data from genres of computer-mediated communication (CMC), social media interactions and Web corpora. The two subtasks of tokenization and part-of-speech tagging were performed on two data sets: (i) a genuine CMC data set with samples from several CMC genres, and (ii) a Web corpora data set of CC-licensed Web pages which represents the type of data found in large corpora crawled from the Web. The teams participating in the shared task achieved a substantial improvement over current off-the-shelf tools for German. The best tokenizer reached an F 1 -score of 99.57% (vs. 98.95% off-the-shelf baseline), while the best tagger reached an accuracy of 90.44% (vs. 84.86% baseline). The gold standard (more than 20,000 tokens of training and test data) is freely available online together with detailed annotation guidelines.

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Section: Motivation Premises and Goalsmentioning

confidence: 99%

EmpiriST 2015: A Shared Task on the Automatic Linguistic Annotation of Computer-Mediated Communication and Web Corpora

Beißwenger

Bartsch

Evert

et al. 2016

Proceedings of the 10th Web as Corpus Workshop

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“…We use the STTS tagset for annotation. Annotation rules for social media characteristics are given in [15], [16] and [17]. The general tagger model is taken from [4] and explained in Subsection III-A.…”

Section: Webtaggermentioning

confidence: 99%

“…Three different types of social media texts are represented, YouTube comments, blog comments, and chat messages. Further corpus statistics can be found in [15].…”

Section: A Corporamentioning

confidence: 99%

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A POS Tagger for Social Media Texts Trained on Web Comments

Neunerdt¹,

Reyer²,

Mathar³

2013

Polibits

Self Cite

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Abstract-Using social media tools such as blogs and forums have become more and more popular in recent years. Hence, a huge collection of social media texts from different communities is available for accessing user opinions, e.g., for marketing studies or acceptance research. Typically, methods from Natural Language Processing are applied to social media texts to automatically recognize user opinions. A fundamental component of the linguistic pipeline in Natural Language Processing is Part-of-Speech tagging. Most state-of-the-art Part-of-Speech taggers are trained on newspaper corpora, which differ in many ways from non-standardized social media text. Hence, applying common taggers to such texts results in performance degradation. In this paper, we present extensions to a basic Markov model tagger for the annotation of social media texts. Considering the German standard Stuttgart/Tübinger TagSet (STTS), we distinguish 54 tag classes. Applying our approach improves the tagging accuracy for social media texts considerably, when we train our model on a combination of annotated texts from newspapers and Web comments.

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“…When NLP tools are used in specific domains, the quality can be increased by developing a domain specific algorithms [3,4,5] or training existing NLP tools on domain specific corpora [6,7]. In both cases an annotated corpus is required to evaluate the output quality of NLP tools in different domains.…”

Section: Introductionmentioning

confidence: 99%

Quick Pad Tagger : An Efficient Graphical User Interface for Building Annotated Corpora with Multiple Annotation Layers

Schreiber

Barkschat

Kraft

et al. 2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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More and more domain specific applications in the internet make use of Natural Language Processing (NLP) tools (e. g. Information Extraction systems). The output quality of these applications relies on the output quality of the used NLP tools. Often, the quality can be increased by annotating a domain specific corpus. However, annotating a corpus is a time consuming and exhaustive task. To reduce the annotation time we present a custom GraphicalUser Interface for different annotation layers.

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Part-Of-Speech Tagging for Social Media Texts

Cited by 26 publications

References 9 publications

EmpiriST 2015: A Shared Task on the Automatic Linguistic Annotation of Computer-Mediated Communication and Web Corpora

EmpiriST 2015: A Shared Task on the Automatic Linguistic Annotation of Computer-Mediated Communication and Web Corpora

A POS Tagger for Social Media Texts Trained on Web Comments

Quick Pad Tagger : An Efficient Graphical User Interface for Building Annotated Corpora with Multiple Annotation Layers

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