TIGER: Linguistic Interpretation of a German Corpus

Brants, Sabine; Dipper, Stefanie; Eisenberg, Peter; Hansen‐Schirra, Silvia; König, Ekkehard; Lezius, Wolfgang; Röhrer, Christian; Smith, George E.; Uszkoreit, Hans

doi:10.1007/s11168-004-7431-3

Cited by 159 publications

(154 citation statements)

References 11 publications

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“…This is significantly higher as the ambiguity in German newspaper texts, e.g., 1 for the TIGER corpus containing 890,000 tokens. In order to provide a sufficiently large training data amount, we combine WebTrain with the TIGER treebank [19] newspaper text corpus. We call that joint-domain training.…”

Section: Resultsmentioning

confidence: 99%

A POS Tagger for Social Media Texts Trained on Web Comments

Neunerdt¹,

Reyer²,

Mathar³

2013

Polibits

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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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Section: Resultsmentioning

confidence: 99%

A POS Tagger for Social Media Texts Trained on Web Comments

Neunerdt¹,

Reyer²,

Mathar³

2013

Polibits

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“…For training and development, the TiGer syntactic treebank 2.2 (Brants et al 2004) was utilized, specifically the 5k train and dev set from the SPMRL 2014 shared task data version (Seddah et al 2014). Importantly, punctuation and other unattached elements are attached to the tree following Maier et al (2012), resolving crossing-branches (for a full description of the data preprocessing, see Seddah et al (2013b)).…”

Section: Methodsmentioning

confidence: 99%

The Devil is in the Details: Parsing Unknown German Words

Dakota

2018

Lecture Notes in Computer Science

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Abstract. The statistical parsing of morphologically rich languages is hindered by the inability of parsers to collect solid statistics because of the large number of word types in such languages. There are however two separate but connected problems, reducing data sparsity of known words and handling rare and unknown words. Methods for tackling one problem may inadvertently negatively impact methods to handle the other. We perform a tightly controlled set of experiments to reduce data sparsity through class-based representations in combination with unknown word signatures with two PCFG-LA parsers that handle rare and unknown words differently on the German TiGer treebank. We demonstrate that methods that have improved results for other languages do not transfer directly to German, and that we can obtain better results using a simplistic model rather than a more generalized model for rare and unknown word handling.

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“…An example of a less widely-used but still well-known parsed corpus is the TiGer corpus [11], of which the current version contains approximately 900 K words/50 K sentences of German newspaper text. TiGer is freely available as plain text for noncommercial, non-profit research purposes and in XML format with phrase-structure and dependency-structure representations.…”

Section: Syntactic Parse Treesmentioning

confidence: 99%

Linguistic Annotation in/for Corpus Linguistics

Gries

Berez

2017

Handbook of Linguistic Annotation

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This article surveys linguistic annotation in corpora and corpus linguistics. We first define the concept of 'corpus' as a radial category and then, in Sect. 2, discuss a variety of kinds of information for which corpora are annotated and that are exploited in contemporary corpus linguistics. Section 3 then exemplifies many current formats of annotation with an eye to highlighting both the diversity of formats currently available and the emergence of XML annotation as, for now, the most widespread form of annotation. Section 4 summarizes and concludes with desiderata for future developments.

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TIGER: Linguistic Interpretation of a German Corpus

Cited by 159 publications

References 11 publications

A POS Tagger for Social Media Texts Trained on Web Comments

A POS Tagger for Social Media Texts Trained on Web Comments

The Devil is in the Details: Parsing Unknown German Words

Linguistic Annotation in/for Corpus Linguistics

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