An adaptable morphological parser for agglutinative languages
Authors: Marina Ermolaeva (Moscow State University, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
The paper reports the state of the ongoing work on creating an adaptable morphological parser for various agglutinative languages. A hybrid approach involving methods typically used for non-agglutinative languages is proposed. We explain the design of a working prototype for inflectional nominal morphology and demonstrate its work with an implementation for Turkish language. An additional experiment of adapting the parser to Buryat (Mongolic family) is discussed.

Introduction
The most obvious way to perform morphological parsing is to make a list of all possible morphological variants of each word. This method has been successfully used for nonagglutinative languages, e.g. (Segalovich 2003) for Russian, Polish and English. Agglutinative languages pose a much more complex task, since the number of possible forms of a single word is theoretically infinite (Jurafsky and Martin 2000). Parsing languages like Turkish often involves designing complicated finite-state machines where each transition corresponds to a single affix (Hankamer 1986; Eryiğit and Adalı 2004; Çöltekin 2010; Sak et al. 2009; Sahin et al. 2013). While these systems can perform extremely well, a considerable redesigning of the whole system is required in order to implement a new language or to take care of a few more affixes. The proposed approach combines both methods mentioned above. A simple finite-state machine allows to split up the set of possible affixes, producing a finite and relatively small set of sequences that can be easily stored in a dictionary. Most systems created for parsing agglutinative languages, starting with (Hankamer 1986) and (Oflazer 1994), process words from left to right: first stem candidates are found in a lexicon, then the remaining part is analyzed. The system presented in this paper applies the right-to-left method (cf. (Eryiğit and Adalı 2004)): affixes are found in the first place. It can ultimately work without a lexicon, in which case the remaining part of the word is assumed to be the stem; to improve precision of parsing, it is possible to compare it to stems contained in a lexicon. A major advantage of right-to-left parsing is the ability to process words with unknown stems without additional computations. Multi-language systems (Akın and Akın 2007; Arkhangelskiy 2012) are a relatively new tendency. With the hybrid approach mentioned above, the proposed system fits within this trend. As the research is still in progress, the working prototype of the parser (written in Python language) is currently restricted to nominal inflectional morphology. Within this scope, it has been implemented for Turkish; an additional experiment with Buryat language is discussed in the section 5.

Conclusion
At the moment, the top-importance task is lifting the temporary limitations of the parser by implementing other parts of speech (finite and non-finite verb forms, pronouns, postpositions etc.) and derivational suffixes. Although the slot system described in 3.1 has been sufficient for both Turkish and Buryat, other agglutinative languages may require more flexibility. This can be achieved either by adding more slots (thus making the slot system nearly universal) or by providing a way to derive the slot system automatically, from plain text or a corpus of tagged texts; the latter solution would also considerably reduce the amount of work that has to be done manually. Another direction of future work involves integrating the parser into a more complex system. DIRETRA, an engine for Turkish-to-English direct translation, is being developed on the base of the parser. The primary goal is to provide a word-for-word translation of a given text, reflecting the morphological phenomena of the source language as precisely as possible. The gloss lines output by the parser are processed by the other modules of the system and ultimately transformed into text representations in the target language:Though the system is being designed for Turkish, the next step planned is to implement other Turkic languages as well.