Tagging and Morphological Disambiguation of Turkish Text

TL;DR

This paper presents a Turkish POS tagger utilizing a comprehensive morphological model, achieving high accuracy and significantly reducing parsing ambiguity and time, with potential applicability to other languages.

Contribution

It introduces a novel Turkish POS tagger based on a detailed morphological specification and disambiguation approach, improving tagging accuracy and parsing efficiency.

Findings

Achieves 98-99% tagging accuracy

Reduces parsing ambiguity by 50%

Speeds up parsing by 2.5 times

Abstract

Automatic text tagging is an important component in higher level analysis of text corpora, and its output can be used in many natural language processing applications. In languages like Turkish or Finnish, with agglutinative morphology, morphological disambiguation is a very crucial process in tagging, as the structures of many lexical forms are morphologically ambiguous. This paper describes a POS tagger for Turkish text based on a full-scale two-level specification of Turkish morphology that is based on a lexicon of about 24,000 root words. This is augmented with a multi-word and idiomatic construct recognizer, and most importantly morphological disambiguator based on local neighborhood constraints, heuristics and limited amount of statistical information. The tagger also has functionality for statistics compilation and fine tuning of the morphological analyzer, such as logging erroneous morphological parses, commonly used roots, etc. Preliminary results indicate that the tagger can tag about 98-99\% of the texts accurately with very minimal user intervention. Furthermore for sentences morphologically disambiguated with the tagger, an LFG parser developed for Turkish, generates, on the average, 50\% less ambiguous parses and parses almost 2.5 times faster. The tagging functionality is not specific to Turkish, and can be applied to any language with a proper morphological analysis interface.