The development of unsupervised learning methods for natural language processing tasks
has become an important and popular area of research. The primary advantage of these …

The amount of unlabeled linguistic data available to us is much larger and growing much
faster than the amount of labeled data. Semi-supervised learning algorithms combine …

Statistical supervised learning techniques have been successful for many natural language
processing tasks, but they require labeled datasets, which can be expensive to obtain. On …

University of Alberta Large-Scale Semi-Supervised Learning for Natural Language Processing
by Shane Bergsma A thesis submitted t Page 1 University of Alberta Large-Scale …

Abstract Part-of-speech (POS) induction is one of the most popular tasks in research on
unsupervised NLP. Many different methods have been proposed, yet comparisons are …

This paper introduces TexSmart, a text understanding system that supports fine-grained
named entity recognition (NER) and enhanced semantic analysis functionalities. Compared …

We address two critical issues involved in applying semi-supervised learning (SSL) to a real-
world task: parameter tuning and choosing which (if any) SSL algorithm is best suited for the …

Semi-supervised learning is an efficient way to improve performance for natural language
processing systems. In this work, we propose Para-SSL, a scheme to generate candidate …

Semi-supervised learning is an efficient method to augment training data automatically from
unlabeled data. Development of many natural language understanding (NLU) applications …

Natural language processing systems such as speech recognition and machine translation
conventionally treat words as their fundamental unit of processing. However, in many cases …