tailieunhanh - Báo cáo khoa học: "Building trainable taggers in a web-based, UIMA-supported NLP workbench"

Argo is a web-based NLP and text mining workbench with a convenient graphical user interface for designing and executing processing workflows of various complexity. The workbench is intended for specialists and nontechnical audiences alike, and provides the ever expanding library of analytics compliant with the Unstructured Information Management Architecture, a widely adopted interoperability framework. | Building trainable taggers in a web-based UIMA-supported NLP workbench Rafal Rak BalaKrishna Kolluru and Sophia Ananiadou National Centre for Text Mining School of Computer Science University of Manchester Manchester Interdisciplinary Biocentre 131 Princess St M1 7dN Manchester UK @ Abstract Argo is a web-based NLP and text mining workbench with a convenient graphical user interface for designing and executing processing workflows of various complexity. The workbench is intended for specialists and nontechnical audiences alike and provides the ever expanding library of analytics compliant with the Unstructured Information Management Architecture a widely adopted interoperability framework. We explore the flexibility of this framework by demonstrating workflows involving three processing components capable of performing self-contained machine learning-based tagging. The three components are responsible for the three distinct tasks of 1 generating observations or features 2 training a statistical model based on the generated features and 3 tagging unlabelled data with the model. The learning and tagging components are based on an implementation of conditional random fields CRF whereas the feature generation component is an analytic capable of extending basic token information to a comprehensive set of features. Users define the features of their choice directly from Argo s graphical interface without resorting to programming a commonly used approach to feature engineering . The experimental results performed on two tagging tasks chunking and named entity recognition showed that a tagger with a generic set of features built in Argo is capable of competing with taskspecific solutions. 1 Introduction The applications of automatic recognition of categories or tagging in natural language processing NLP range from part of speech tagging to chunking to named entity recognition and complex scientific discourse .