Inclusions from other languages can be a significant source of errors for monolin-gual parsers. We show this for English in-clusions, which are sufficiently frequent to present a problem when parsing German. We describe an annotation-free approach for accurately detecting such inclusions, and de-velop two methods for interfacing this ap-proach with a state-of-the-art parser for Ger-man. An evaluation on the TIGER cor-pus shows that our inclusion entity model achieves a performance gain of 4.3 points in F-score over a baseline of no inclusion de-tection, and even outperforms a parser with access to gold standard part-of-speech tags.
Learning a foreign language requires much practice outside of the classroom. Computer-assisted langu...
This paper presents an original approach to part-of-speech tagging of fine-grained features (such as...
We explore the ability to perform automatic prosodic analysis in one language using models trained o...
We present an unsupervised system that exploits linguistic knowledge resources, namely English and G...
Abstract. The use of lexicons and corpora advances both linguistic re-search and performance of curr...
This paper presents an unsupervised system that classies English inclusions in written text. It will...
Statistical parsing research can be described as being anglo-centric: new models are first proposed ...
In this paper, we present an unlexicalized parser for German which employs smoothing and suffix an...
This paper presents a comparative study of probabilistic treebank parsing of Ger-man, using the Negr...
Generative lexicalized parsing models, which are the mainstay for probabilistic parsing of English, ...
We present a parser for German that achieves a competitive accuracy on unrestricted input while mai...
Recent studies focussed on the question whether less-configurational languages like German are harde...
In this paper, a two-stage partial parser for untagged German sentences is presented. In the first s...
The Constituent Likelihood Automatic Word-tagging System (CLAWS) was originally designed for the low...
We describe the collection of transcription corrections and grammatical error annotations for the Cr...
Learning a foreign language requires much practice outside of the classroom. Computer-assisted langu...
This paper presents an original approach to part-of-speech tagging of fine-grained features (such as...
We explore the ability to perform automatic prosodic analysis in one language using models trained o...
We present an unsupervised system that exploits linguistic knowledge resources, namely English and G...
Abstract. The use of lexicons and corpora advances both linguistic re-search and performance of curr...
This paper presents an unsupervised system that classies English inclusions in written text. It will...
Statistical parsing research can be described as being anglo-centric: new models are first proposed ...
In this paper, we present an unlexicalized parser for German which employs smoothing and suffix an...
This paper presents a comparative study of probabilistic treebank parsing of Ger-man, using the Negr...
Generative lexicalized parsing models, which are the mainstay for probabilistic parsing of English, ...
We present a parser for German that achieves a competitive accuracy on unrestricted input while mai...
Recent studies focussed on the question whether less-configurational languages like German are harde...
In this paper, a two-stage partial parser for untagged German sentences is presented. In the first s...
The Constituent Likelihood Automatic Word-tagging System (CLAWS) was originally designed for the low...
We describe the collection of transcription corrections and grammatical error annotations for the Cr...
Learning a foreign language requires much practice outside of the classroom. Computer-assisted langu...
This paper presents an original approach to part-of-speech tagging of fine-grained features (such as...
We explore the ability to perform automatic prosodic analysis in one language using models trained o...