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Erschienen in:
Turkish and Its Challenges for Language and Speech Processing Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

7. Dependency Parsing of Turkish

verfasst von : Gülşen Eryiğit, Joakim Nivre, Kemal Oflazer

Erschienen in: Turkish Natural Language Processing

Verlag: Springer International Publishing

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Abstract

Syntactic parsing is the process of taking an input sentence and producing an appropriate syntactic structure for it. It is a crucial stage in that it provides a way to pass from core NLP tasks to the semantic layer and it has been shown to increase the performance of many high-tier NLP applications such as machine translation, sentiment analysis, question answering, and so on. Statistical dependency parsing with its high coverage and easy-to-use outputs has become very popular in recent years for many languages including Turkish. In this chapter, we describe the issues in developing and evaluating a dependency parser for Turkish, which poses interesting issues and many different challenges due to its agglutinative morphology and freeness of its constituent order. Our approach is an adaptation of a language-independent data-driven statistical parsing system to Turkish.

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Vorheriges Kapitel Turkish Named-Entity Recognition
Nächstes Kapitel Wide-Coverage Parsing, Semantics, and Morphology
Fußnoten
1
Please note that arrows in this representations point from dependents to heads and we do not include punctuation in dependency relations.
 
2
We however do not necessarily suggest that the morphological sub-lexical representation that we use for Turkish later in this paper is applicable to these languages.
 
3
In Turkish, such sentences are called “inverted sentences” and are mostly used in spoken language but rarely in written form.
 
4
+A3sg: Third person singular agreement, +P2pl: Second person plural possessive agreement, +Loc: Locative Case.
 
5
Bozşahin (2002) uses morphemes as sub-lexical constituents in a CCG framework. Since the lexicon was organized in terms of morphemes each with its own CCG functor, the grammar had to account for both the morphotactics and the syntax at the same time.
 
6
Experiments have also been performed using memory-based learning (Daelemans and van den Bosch 2005). They were found to give lower parsing accuracy.
 
7
A recent study by Sulubacak and Eryiğit (2013) extends this representation and assigns different lemma and surface form information for each IG.
 
8
The token indexes within the actual token sequence are represented by their relative positions to the stack and queue elements. In this representation σ 0 + 1 refers directly to the right neighbor of the σ 0 within the actual sequence. Similarly, σ 0 − 1 refers to the left neighbor.
 
9
Actually, there are two parsers (Bick and Attardi in Table 7.2) in this group that try to use parts of the inflectional features under special circumstances.
 
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Metadaten
Titel
Dependency Parsing of Turkish
verfasst von
Gülşen Eryiğit
Joakim Nivre
Kemal Oflazer
Copyright-Jahr
2018
Buch
Turkish Natural Language Processing

Print ISBN: 978-3-319-90163-3

Electronic ISBN: 978-3-319-90165-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-90165-7_7

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