25 research outputs found
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
Robustnà parsing zašuměného obsah
AÄŤkoli Ăşspěšnost syntaktickĂ© analĂ˝zy (parsingu) domĂ©novÄ› shodnĂ˝ch textĹŻ se v poslednĂch letech soustavnÄ› zvyšuje, texty mimo trĂ©novacĂ domĂ©nu a gramaticky problematickĂ© texty nadále vzdorujĂ a ÄŤasto na nich pozorujeme vĂ˝raznĂ˝ pokles v kvalitÄ›. V tĂ©to práci se zaměřujeme na analĂ˝zu "zašumÄ›nĂ©ho" vstupu pocházejĂcĂho ze sluĹľeb, jako je Twitter. Zkoumáme otázku, zda pĹ™edzpracovánĂ textu zaloĹľenĂ© na strojovĂ©m pĹ™ekladu a neĹ™ĂzenĂ˝ch normalizaÄŤnĂch modelech mĹŻĹľe zvýšit Ăşspěšnost analĂ˝zy takovĂ˝ch dat. ZkoumanĂ© postupy vyhodnocujeme na existujĂcĂch testovacĂch datech, kromÄ› toho jsme vytvoĹ™ili i vlastnĂ data pro závislostnĂ syntaktickou analĂ˝zu zašumÄ›nĂ˝ch dat z Twitteru. Ukazujeme, Ĺľe normalizace textu kombinovaná s obecnĂ˝mi i domĂ©novÄ› zaměřenĂ˝mi taggery mĹŻĹľe vĂ©st k vĂ˝znamnĂ©mu zlepšenĂ 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 LinguisticsĂšstav formálnĂ a aplikovanĂ© lingvistikyFaculty of Mathematics and PhysicsMatematicko-fyzikálnĂ 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