A resource-light approach to morpho-syntactic tagging

Abstract

Morpho-syntactic tagging is the process of assigning part of speech (POS), case, number, gender, and other morphological information to each word in a corpus. Morpho-syntactic tagging is an important step in natural language processing. Corpora that have been morphologically tagged are very useful both for linguistic research, e.g. finding instances or frequencies of particular constructions in large corpora, and for further computational processing, such as syntactic parsing, speech recognition, stemming, and word-sense disambiguation, among others. Despite the importance of morphological tagging, there are many languages that lack annotated resources. This is almost inevitable because these resources are costly to create. But, as described in this thesis, it is possible to avoid this expense. This thesis describes a method for transferring annotation from a morphologically annotated corpus of a source language to a corpus of a related target language. Unlike unsupervised approaches that do not require annotated data at all and, as a consequence, lack precision, the approach proposed in this dissertation relies on linguistic knowledge, but avoids large-scale grammar engineering. The approach needs neither a parallel corpus nor a bilingual lexicon, and requires much less linguistic labor than the standard technology. This dissertation describes experiments with Russian, Czech, Polish, Spanish, Portuguese, and Catalan. However, the general method proposed can be applied to any fusional language.

MULTEXT-East: morphosyntactic resources for Central and Eastern European languages

“…The MULTEXT-East morphosyntactic specifications currently lack consistency between the languages (Przepiórkowski and Woliński 2003;Derzhanski and Kotsyba 2009;Feldman and Hana 2010), and a typology of the problems is summarised in Rosen (2010). Problematic cases are divided into those where different features in various languages are used to describe the same phenomenon, and, conversely, the same features are used to describe different phenomena.…”

Arabic Part of Speech Tagging Using K-Nearest Neighbour and Naive Bayes Classifiers Combination

“…Fine-grained POS (morpho-syntactic or morphological) tagging is the procedure of determining POS, tense, number, gender and other morphological information for every single word in a sentence (Feldman, 2006;Schmid and Laws, 2008). POS tagging is an essential language analysis task in almost all NLP systems, including information extraction, corpus annotation Science Publications JCS projects, word-sense disambiguation and etc.…”

Resource‐Light Approaches to Computational Morphology Part 1: Monolingual Approaches

“…At the same time, no manually compiled lexicon can cover an arbitrary text. Feldman and Hana (2010) document this on a statistic of Czech nouns obtained from the PDT corpus (Hajič et al. 2000).…”

“…2008; Oflazer et al. 2001), and manual encoding of basic linguistic facts (e.g., Cucerzan and Yarowsky 2002; Feldman and Hana 2010; Tepper and Xia 2010). Learning from a different language (e.g., Bosch et al.…”

“…Learning from a different language (e.g., Bosch et al. 2008; Cucerzan and Yarowsky 2002; Feldman and Hana 2010), another resource‐light strategy, will be discussed in our forthcoming survey (Feldman and Hana forthcoming).…”