Morphology-Syntax interface for Turkish LFG

This paper investigates the use of sublexical units as a solution to handling the complex morphology with productive derivational processes, in the development of a lexical functional grammar for Turkish. Such sublexical units make it possible to expose the internal structure of words with multiple derivations to the grammar rules in a uniform manner. This in turn leads to more succinct and manageable rules. Further, the semantics of the derivations can also be systematically reflected in a compositional way by constructing PRED values on the fly. We illustrate how we use sublexical units for handling simple productive derivational morphology and more interesting cases such as causativization, etc., which change verb valency. Our priority is to handle several linguistic phenomena in order to observe the effects of our approach on both the c-structure and the f-structure representation, and grammar writing, leaving the coverage and evaluation issues aside for the moment

Building a wordnet for Turkish

This paper summarizes the development process of a wordnet for Turkish as part of the Balkanet project. After discussing the basic method-ological issues that had to be resolved during the course of the project, the paper presents the basic steps of the construction process in chronological order. Two applications using Turkish wordnet are summarized and links to resources for wordnet builders are provided at the end of the paper

Comparing the use of edited and unedited text in parser self-training

We compare the use of edited text in the form of newswire and unedited text in the form of discussion forum posts as sources for training material in a self-training experiment involving the Brown reranking parser and a test set of sentences from an online sports discussion forum. We find that grammars induced from the two automatically parsed corpora achieve similar Parseval f-scores, with the grammars induced from the discussion forum material being slightly superior. An error analysis reveals that the two types of grammars do behave differently

Improving dependency label accuracy using statistical post-editing: A cross-framework study

We present a statistical post-editing method for modifying the dependency labels in a dependency analysis. We test the method using two English datasets, three parsing systems and three labelled dependency schemes. We demonstrate how it can be used both to improve dependency label accuracy in parser output and highlight problems with and differences between constituency-to-dependency conversions

Altsözcüksel birimlerle Türkçe için sözcüksel işlevsel gramer geliştirilmesi

Bu bildiri Türkçe’nin karmaşık biçimbilimsel yapısı ve zengin türetme olaylarını ele alırken bir çözüm olarak altsözcüksel birimler kullanmayı incelemekte ve önerilen yaklaşımı Pargram projesi dahilinde gerçeklenmekte olan Türkçe sözcüksel işlevsel gramer üzerinden anlatmaktadır. İzlediğimiz yaklaşım sayesinde kurallar daha düzenli ve özlü bir şekilde yazılabilmekte, böylece hem genelleme imkanı arttığı için daha az sayıda olan hem de içerik olarak karmaşık olmayan kuralarla gramer kapsamı genişletilebilmektedir. Üstelik türetmelerin sözcüklere anlambilimsel katkıları programın çalışması sırasında yaratılan PRED değerleri sayesinde sistematik bir biçimde ifade edilebilmektedir. Çalışmamız altsözcüksel birimlerin basit yapım ekleri ile kullanımına yer vermekte daha sonra ettirgen yapılar gibi görece daha karmaşık dil olaylarına değinmektedir. Öncelikli amacımız kullandığımız yaklaşımı mümkün olduğunca birbirinden farklı dilbilimsel alanlarda incelemek olduğu için bu bildiride sayısal bir değerlendirmeye yer verilmemiştir

Low-resource machine translation using MATREX: The DCU machine translation system for IWSLT 2009

In this paper, we give a description of the Machine Translation (MT) system developed at DCU that was used for our fourth participation in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT 2009). Two techniques are deployed in our system in order to improve the translation quality in a low-resource scenario. The first technique is to use multiple segmentations in MT training and to utilise word lattices in decoding stage. The second technique is used to select the optimal training data that can be used to build MT systems. In this year’s participation, we use three different prototype SMT systems, and the output from each system are combined using standard system combination method. Our system is the top system for Chinese–English CHALLENGE task in terms of BLEU score

Lemmatization and lexicalized statistical parsing of morphologically rich languages: the case of French

This paper shows that training a lexicalized parser on a lemmatized morphologically-rich treebank such as the French Treebank slightly improves parsing results. We also show that lemmatizing a similar in size subset of the English Penn Treebank has almost no effect on parsing performance with gold lemmas and leads to a small drop of performance when automatically assigned lemmas and POS tags are used. This highlights two facts: (i) lemmatization helps to reduce lexicon data-sparseness issues for French, (ii) it also makes the parsing process sensitive to correct assignment of POS tags to unknown words

LFG without C-structures

We explore the use of two dependency parsers, Malt and MST, in a Lexical Functional Grammar parsing pipeline. We compare this to the traditional LFG parsing pipeline which uses constituency parsers. We train the dependency parsers not on classical LFG f-structures but rather on modified dependency-tree versions of these in which all words in the input sentence are represented and multiple heads are removed. For the purposes of comparison, we also modify the existing CFG-based LFG parsing pipeline so that these "LFG-inspired" dependency trees are produced. We find that the differences in parsing accuracy over the various parsing architectures is small

Irish treebanking and parsing: a preliminary evaluation

Language resources are essential for linguistic research and the development of NLP applications. Low- density languages, such as Irish, therefore lack significant research in this area. This paper describes the early stages in the development of new language resources for Irish – namely the first Irish dependency treebank and the first Irish statistical dependency parser. We present the methodology behind building our new treebank and the steps we take to leverage upon the few existing resources. We discuss language specific choices made when defining our dependency labelling scheme, and describe interesting Irish language characteristics such as prepositional attachment, copula and clefting. We manually develop a small treebank of 300 sentences based on an existing POS-tagged corpus and report an inter-annotator agreement of 0.7902. We train MaltParser to achieve preliminary parsing results for Irish and describe a bootstrapping approach for further stages of development

From news to comment: Resources and benchmarks for parsing the language of web 2.0

We investigate the problem of parsing the noisy language of social media. We evaluate four all-Street-Journal-trained statistical parsers (Berkeley, Brown, Malt and MST) on a new dataset containing 1,000 phrase structure trees for sentences from microblogs (tweets) and discussion forum posts. We compare the four parsers on their ability to produce Stanford dependencies for these Web 2.0 sentences. We find that the parsers have a particular problem with tweets and that a substantial part of this problem is related to POS tagging accuracy. We attempt three retraining experiments involving Malt, Brown and an in-house Berkeley-style parser and obtain a statistically significant improvement for all three parsers