Distributed peer review enhanced with natural language processing and machine learning

Machine Learning in Natural Language Processing

This thesis examines the use of machine learning techniques in various tasks of natural language processing, mainly for the task of information extraction from texts. The objectives are the improvement of adaptability of information extraction systems to new thematic domains (or even languages), and the improvement of their performance using as fewer resources (either linguistic or human) as po...

Machine Learning in Natural Language Processing

Machine learning is concerned with acquiring knowledge from an environment in a computational manner, in order to improve the performance. (See Langley [15] for overview of ML.) Many forces over the past couple of decades have seen the blending of ML and NLP become increasingly common. The ever expanding availability of large corpora; more powerful computing resources; and a greater demand for ...

Machine Learning in Natural Language Processing

2 Tagging 5 2.1 PoS tagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 Features for PoS Tagging . . . . . . . . . . . . . . . . 6 2.1.2 PoS Tagging Results for Different Languages . . . . . 7 2.2 Named Entity Recognition . . . . . . . . . . . . . . . . . . . . 8 2.2.1 One-step vs Two-step Classification . . . . . . . . . . 8 2.2.2 Features for NER . . . . . . . . . . . . . ...

Knowledge management using machine learning, natural language processing and ontology

Evaluation of Machine Learning Methods for Natural Language Processing Tasks

We show that the methodology currently in use for comparing symbolic supervised learning methods applied to human language technology tasks is unreliable. We show that the interaction between algorithm parameter settings and feature selection within a single algorithm often accounts for a higher variation in results than differences between different algorithms or information sources. We illust...