Robust Parsing of Noisy Content

While parsing performance on in-domain text has developed steadily in recent years, out-of-domain text and grammatically noisy text remain an obstacle and often lead to significant decreases in parsing accuracy. In this thesis, we focus on the parsing of noisy content, such as user-generated content in services like Twitter. We investigate the question whether a preprocessing step based on machine translation techniques and unsupervised models for text-normalization can improve parsing performance on noisy data. Existing data sets are evaluated and a new data set for dependency parsing of grammatically noisy Twitter data is introduced. We show that text-normalization together with a combination of domain-specific and generic part-of-speech taggers can lead to a significant improvement in parsing accuracy. Powered by TCPDF (www.tcpdf.org

Robustní parsing zašuměného obsah

Ačkoli úspěšnost syntaktické analýzy (parsingu) doménově shodných textů se v posledních letech soustavně zvyšuje, texty mimo trénovací doménu a gramaticky problematické texty nadále vzdorují a často na nich pozorujeme výrazný pokles v kvalitě. V této práci se zaměřujeme na analýzu "zašuměného" vstupu pocházejícího ze služeb, jako je Twitter. Zkoumáme otázku, zda předzpracování textu založené na strojovém překladu a neřízených normalizačních modelech může zvýšit úspěšnost analýzy takových dat. Zkoumané postupy vyhodnocujeme na existujících testovacích datech, kromě toho jsme vytvořili i vlastní data pro závislostní syntaktickou analýzu zašuměných dat z Twitteru. Ukazujeme, že normalizace textu kombinovaná s obecnými i doménově zaměřenými taggery může vést k významnému zlepšení kvality parsingu. Powered by TCPDF (www.tcpdf.org)While parsing performance on in-domain text has developed steadily in recent years, out-of-domain text and grammatically noisy text remain an obstacle and often lead to significant decreases in parsing accuracy. In this thesis, we focus on the parsing of noisy content, such as user-generated content in services like Twitter. We investigate the question whether a preprocessing step based on machine translation techniques and unsupervised models for text-normalization can improve parsing performance on noisy data. Existing data sets are evaluated and a new data set for dependency parsing of grammatically noisy Twitter data is introduced. We show that text-normalization together with a combination of domain-specific and generic part-of-speech taggers can lead to a significant improvement in parsing accuracy. Powered by TCPDF (www.tcpdf.org)Institute of Formal and Applied LinguisticsÚstav formální a aplikované lingvistikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

The Denoised Web Treebank: Evaluating Dependency Parsing under Noisy Input Conditions

We introduce the Denoised Web Treebank: a treebank including a normalization layer and a corresponding evaluation metric for dependency parsing of noisy text, such as Tweets. This benchmark enables the evaluation of parser robustness as well as text normalization methods, including normalization as machine translation and unsupervised lexical normalization, directly on syntactic trees. Experiments show that text normalization together with a combination of domain-specific and generic part-of-speech taggers can lead to a significant improvement in parsing accuracy on this test set

The Denoised Web Treebank: Evaluating Dependency Parsing under Noisy Input Conditions

We introduce the Denoised Web Treebank: a treebank including a normalization layer and a corresponding evaluation metric for dependency parsing of noisy text, such as Tweets. This benchmark enables the evaluation of parser robustness as well as text normalization methods, including normalization as machine translation and unsupervised lexical normalization, directly on syntactic trees. Experiments show that text normalization together with a combination of domain-specific and generic part-of-speech taggers can lead to a significant improvement in parsing accuracy on this test set