TAILIEUCHUNG - Báo cáo khoa học: "Automatic Detection and Correction of Errors in Dependency Treebanks"

Annotated corpora are essential for almost all NLP applications. Whereas they are expected to be of a very high quality because of their importance for the followup developments, they still contain a considerable number of errors. With this work we want to draw attention to this fact. Additionally, we try to estimate the amount of errors and propose a method for their automatic correction. | Automatic Detection and Correction of Errors in Dependency Treebanks Alexander Volokh DFKI Stuhlsatzenhausweg 3 66123 Saarbrucken Germany Gunter Neumann DFKI Stuhlsatzenhausweg 3 66123 Saarbrucken Germany neumann@ Abstract Annotated corpora are essential for almost all NLP applications. Whereas they are expected to be of a very high quality because of their importance for the followup developments they still contain a considerable number of errors. With this work we want to draw attention to this fact. Additionally we try to estimate the amount of errors and propose a method for their automatic correction. Whereas our approach is able to find only a portion of the er -rors that we suppose are contained in almost any annotated corpus due to the nature of the process of its creation it has a very high pre -cision and thus is in any case beneficial for the quality of the corpus it is applied to. At last we compare it to a different method for error detection in treebanks and find out that the errors that we are able to detect are mostly different and that our approaches are complementary. 1 Introduction Treebanks and other annotated corpora have become essential for almost all NLP applications. Papers about corpora like the Penn Treebank 1 have thousands of citations since most of the algorithms profit from annotated data during the development and testing and thus are widely used in the field. Treebanks are therefore expected to be of a very high quality in order to guarantee reliability for their theoretical and practical uses. The construction of an annotated corpus involves a lot of work performed by large groups. However despite the fact that a lot of human post-editing and automatic quality assurance is done errors can not be avoided completely 5 . 346 In this paper we propose an approach for finding and correcting errors in dependency treebanks. We apply our method to the English dependency corpus - conversion of the Penn .

• Báo cáo khoa học
• Automatic Detection
• Correction of Errors
• Dependency Treebanks
• báo cáo khoa học
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Design of an automatic three phase sequence reversal detection
• Development of an automatic three phase sequence reversal detection
• Correction for industrial equipment structure
• Three phase sequence reversal detection
• Automatic Extraction of Lexico Syntactic Patterns
• Detection of Negation
• Speculation Scopes
• Semi supervised Learning
• Automatic Prosodic Event Detection
• Co training Algorithm
• xử lý ngôn ngữ tự nhiên
• Using Deep Morphology
• Improve Automatic Error Detection in Arabic Handwriting Recognition
• Nizar Habash and Ryan M
• Roth
• Automatic Satire Detection
• Are You Having a Laugh
• Clint Burfoot
• Syllable Boundaries Combining the Advantages of Treebank
• Bracketed Corpora Training
• mô hình ngôn ngữ
• Automatic error detection
• the Japanese learners’ English
• spoken data
• Grammar Elements
• Decrease Readability
• Improved Automatic
• Detection of Zero Subjects
• Impersonal Constructions in Spanish
• scientific reports
• model language
• process natural language
• Text Genre
• Brett Xerox
• Automatic Detection of Nonreferential
• Spoken Multi Party Dialog
• Christoph Muller
• Luận án Tiến sĩ
• Atomic and nuclear physics
• Luận án Tiến sĩ Vật lý
• Spect CT images segmentation software
• Brain tumors
• PPE detection
• Deep learning
• Object detection
• Automatic monitoring
• Real time personal protective equipment detection
• Tạp chí khoa học
• Automatic detection of surface water bodies
• Sentinel 1 SAR images using Valley Emphasis method
• The highest Completeness
• Surface water body
• Valley emphasis algorithm
• Automatic Detectio
• Poor Speech Recognition
• Dialogue Level
• BMC Bioinformatics
• Synapse detection
• Automatic quantification
• 3D confocal image
• Model selection
• High throughput microscopy
• Analyze cellular dynamics
• Requires automated methods
• Automatic processing
• Research Challenges
• Satellite Images
• Mine Detection
• Chemical Unmanned
• Aerial Vehicle
• Báo cáo sinh học hay
• cách trình bày báo cáo
• báo cáo sinh học
• công trình nghiên cứu sinh học
• tài liệu về sinh học
• báo cáo hóa học
• công trình nghiên cứu về hóa học
• tài liệu về hóa học
• Báo cáo lâm nghiệp hay
• báo cáo lâm nghiệp
• công trình nghiên cứu lâm nghiệp
• tài liệu về lâm nghiệp