Two Approaches for Building an Unsupervised Dependency Parser and their Other Applications

Much work has been done on building a parser for natural languages, but most of this work has concentrated on supervised parsing. Unsupervised parsing is a less explored area, and unsupervised dependency parser has hardly been tried. In this paper we present two approaches for building an unsupervised dependency parser. One approach is based on learning dependency relations and the other on learning subtrees. We also propose some other applications of these approaches

Proceedings of the Morpho Challenge 2010 Workshop

In natural language processing many practical tasks, such as speech recognition, information retrieval and machine translation depend on a large vocabulary and statistical language models. For morphologically rich languages, such as Finnish and Turkish, the construction of a vocabulary and language models that have a sufficient coverage is particularly difficult, because of the huge amount of different word forms. In Morpho Challenge 2010 unsupervised and semi-supervised algorithms are suggested to provide morpheme analyses for words in different languages and evaluated in various practical applications. As a research theme, unsupervised morphological analysis has received wide attention in conferences and scientific journals focused on computational linguistic and its applications. This is the proceedings of the Morpho Challenge 2010 Workshop that contains one introduction article with a description of the tasks, evaluation and results and six articles describing the participating unsupervised and supervised learning algorithms. The Morpho Challenge 2010 Workshop was held at Espoo, Finland in 2-3 September, 2010.reviewe

Some Salient Issues in the Unsupervised Learning of Igbo Morphology

The issue of automatic learning of the morphology of natural language is an important topic in computational linguistics. This owes to the fact that morphology is foundational to the study of linguistics. In addition, the emerging information society demands the application of Information and Communication Technologies (ICT) to languages in ways that demand human-like analysis of language and this depends to a large extent on the ability to undertake computational analysis of morphology. Even though rule-based and supervised learning approaches to the modeling of morphology have been found to be productive, they have also been discovered to be costly, cumbersome and sucseptible to human errors. Contrarily, unsupervised learning methods do not require the expensive human intervention but as in everything statistical, they demand large volumes of linguistic data. This poses a challenge to resource scarce languages such as Igbo. Furthermore, being a highly agglutinative language, Igbo features certain morphological processes that may not be easily accommodated by most of the frequency-driven unsupervised learning models available. this paper takes a critical look at some of the identified challenges of inducing Igbo morphology as a first step in devising methods by which they can be addressed