In recent years, there has been an increased interest in minimizing the need for annotated data in NLP. Significant progress has been made in the development of both semi-supervised and unsupervised learning approaches. Although unsupervised approaches have proved more challenging than semi-supervised ones, their further development is particularly important because they carry the highest potential in terms of avoiding annotation cost.

Such approaches can be applied to any language or genre for which adequate raw text resources are available. They also bear theoretical promise for their ability to recover novel, valuable information in textual data and to expose underlying relations between form and various linguistic phenomena. Largely due to these benefits, NLP has recently experienced a surge of interest in unsupervised learning techniques. Increasingly sophisticated approaches have been proposed and applied to a wide range of tasks, including parsing, verb clustering, induction of grammatical categories, lexical semantics, POS tagging, and many others.

The aim of this workshop is to bring together researchers working on different areas of unsupervised language learning. The objective is to summarize what has been achieved in the topic, to foster discussions on current problems in the area, and to discuss future trends. 

The workshop will be held in conjunction with EMNLP 2011, Edinburgh, Scotland in July 30.


The workshop's program has been posted.