TAILIEUCHUNG - Báo cáo khoa học: "Named Entity Recognition without Gazetteers"

It is often claimed that Named Entity recognition systems need extensive gazetteers--lists of names of people, organisations, locations, and other named entities. Indeed, the compilation of such gazetteers is sometimes mentioned as a bottleneck in the design of Named Entity recognition systems. We report on a Named Entity recognition system which combines rule-based grammars with statistical (maximum entropy) models. We report on the system's performance with gazetteers of different types and different sizes, using test material from the MUC-7 competition. . | Proceedings of EACL 99 Named Entity Recognition without Gazetteers Andrei Mikheev Marc Moens and Claire Grover HCRC Language Technology Group University of Edinburgh 2 Buccleuch Place Edinburgh EH8 9LW UK. mikheev@ Abstract It is often claimed that Named Entity recognition systems need extensive gazetteers lists of names of people organisations locations and other named entities. Indeed the compilation of such gazetteers is sometimes mentioned as a bottleneck in the design of Named Entity recognition systems. We report on a Named Entity recognition system which combines rule-based grammars with statistical maximum entropy models. We report on the system s performance with gazetteers of different types and different sizes using test material from the MUC-7 competition. We show that for the text type and task of this competition it is sufficient to use relatively small gazetteers of well-known names rather than large gazetteers of low-frequency names. We conclude with observations about the domain independence of the competition and of our experiments. 1 Introduction Named Entity recognition involves processing a text and identifying certain occurrences of words or expressions as belonging to particular categories of Named Entities ne ne recognition software serves as an important preprocessing tool for tasks such as information extraction information retrieval and other text processing applications. What counts as a Named Entity depends on the application that makes use of the annotations. One such application is document retrieval or automated document forwarding documents an-noted with NE information can be searched more Now also at Harlequin Ltd. Edinburgh office accurately than raw text. For example NE annotation allows you to search for all texts that mention the company Philip Morris ignoring documents about a possibly unrelated person by the same name. Or you can have all documents forwarded to you about a .

• Báo cáo khoa học
• Named Entity Recognition
• Gazetteers
• Andrei Mikheev
• Marc Moens
• scientific reports
• model language
• process natural language
• BMC Bioinformatics
• Multi task learning
• Cross sharing structure
• Biomedical named entity recognition
• CoNLL format
• Named entity
• Shared task
• VLSP workshop
• Entity recognition in Vietnamese texts
• Character level deep lifelong learning model
• Deep lifelong learning
• DeepLML for NER in Vietnamese texts
• Incorporating speech recognition confidence
• discriminative named entity recognition
• speech data
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Protein families
• Named entity linking
• Event extraction
• Natural language processing
• Multilingual Named Entity Recognition using Parallel Data and Metadata from Wikipedia
• Hwanjo Yu
• Long Papers
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Joint Inference
• Normalization for Tweets
• Hierarchical Joint Learning
• Improving Joint Parsing and Named Entity Recognition
• Non Jointly Labeled Data
• Arabic Named Entity Recognition
• Using Features Extracted
• Noisy Data
• Exploiting Morphology
• Turkish Named Entity Recognition System
• Reyyan Yeniterzi
• Using Search Engines
• Robust Cross Domain Named Entity Recognition
• Massimiliano Ciaramita Stefan Rud
• Exploiting Feature Hierarchy
• Transfer Learning
• Inducing Gazetteers
• Large scale Clustering
• Dependency Relations
• Mining Wiki Resources
• Multilingual Named Entity Recognition
• Alexander E
• Richman
• Improving the Scalability
• Semi Markov Conditional Random Fields
• An Effective Two Stage Model
• Exploiting Non Local Dependencies
• Semantic enrichment
• journal articles
• chemical named entity recognition
• Multi Criteria based Active Learning
• Dan Shen
• Japanese Named Entity Recognition
• a Simple Rule Generator
• Decision Tree Learning
• Using Machine Learning to Maintain Rule based Named Entity Recognition
• Classification Systems
• Georgios Petasis
• an HMM based Chunk Tagger
• GuoDong Zhou Jian Su
• Teaching a Weaker Classifier
• Upper Case Text
• Bootstrapping Named Entity Recognition
• Automatically Generated Gazetteer Lists
• Zornitsa Kozareva
• Clique Based Clustering
• improving Named Entity
• Recognition systems
• A Framework for Unifying
• Disambiguation Extraction Tools