An Application of the PDT-scheme to a Parallel Treebank

Proceedings of the Sixth International Workshop on Treebanks and Linguistic Theories. Editors: Koenraad De Smedt, Jan Hajič and Sandra Kübler. NEALT Proceedings Series, Vol. 1 (2007), 163-174. © 2007 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/4476

Translation of "It" in a Deep Syntax Framework

We present a novel approach to the translation of the English personal pronoun it to Czech. We conduct a linguistic analysis on how the distinct categories of it are usually mapped to their Czech counterparts. Armed with these observations, we design a discriminative translation model of it, which is then integrated into the TectoMT deep syntax MT framework. Features in the model take advantage of rich syntactic annotation TectoMT is based on, external tools for anaphoricity resolution, lexical co-occurrence frequencies measured on a large parallel corpus and gold coreference annotation. Even though the new model for it exhibits no improvement in terms of BLEU, manual evaluation shows that it outperforms the original solution in 8.5% sentences containing it

New Language Pairs in TectoMT

The TectoMT tree-to-tree machine translation system has been updated this year to support easier retraining for more translation directions. We use multilingual standards for morphology and syntax annotation and language-independent base rules. We include a simple, non-parametric way of combining TectoMT’s transfer model outputs

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010

Ensemble Parsing and its Effect on Machine Translation

The focus of much of dependency parsing is on creating new modeling techniques and examining new feature sets for existing dependency models. Often these new models are lucky to achieve equivalent results with the current state of the art results and often perform worse. These approaches are for languages that are often resource-rich and have ample training data available for dependency parsing. For this reason, the accuracy scores are often quite high. This, by its very nature, makes it quite difficult to create a significantly large increase in the current state-of-the-art. Research in this area is often concerned with small accuracy changes or very specific localized changes, such as increasing accuracy of a particular linguistic construction. With so many modeling techniques available to languages with large resources the problem exists on how to exploit the current techniques with the use of combination, or ensemble, techniques along with this plethora of data. Dependency parsers are almost ubiquitously evaluated on their accuracy scores, these scores say nothing of the complexity and usefulness of the resulting structures. The structures may have more complexity due to the depth of their co- ordination or noun phrases. As dependency parses are basic structures in which other systems are built upon, it would seem more reasonable to judge these parsers down the NLP pipeline. The types of parsing errors that cause significant problems in other NLP applications is currently an unknown

Proceedings

Proceedings of the Ninth International Workshop on Treebanks and Linguistic Theories. Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. NEALT Proceedings Series, Vol. 9 (2010), 268 pages. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15891

Complex Predicates annotation in a corpus of Portuguese

We present an annotation scheme for the annotation of complex predicates, understood as constructions with more than one lexical unit, each contributing part of the information normally associated with a single predicate. We discuss our annotation guidelines of four types of complex predicates, and the treatment of several difficult cases, related to ambiguity, overlap and coordination. We then discuss the process of marking up the Portuguese CINTIL corpus of 1M tokens (written and spoken) with a new layer of information regarding complex predicates. We also present the outcomes of the annotation work and statistics on the types of CPs that we found in the corpus.info:eu-repo/semantics/publishedVersio

Porting a lexicalized-grammar parser to the biomedical domain

AbstractThis paper introduces a state-of-the-art, linguistically motivated statistical parser to the biomedical text mining community, and proposes a method of adapting it to the biomedical domain requiring only limited resources for data annotation. The parser was originally developed using the Penn Treebank and is therefore tuned to newspaper text. Our approach takes advantage of a lexicalized grammar formalism, Combinatory Categorial Grammar (ccg), to train the parser at a lower level of representation than full syntactic derivations. The ccg parser uses three levels of representation: a first level consisting of part-of-speech (pos) tags; a second level consisting of more fine-grained ccg lexical categories; and a third, hierarchical level consisting of ccg derivations. We find that simply retraining the pos tagger on biomedical data leads to a large improvement in parsing performance, and that using annotated data at the intermediate lexical category level of representation improves parsing accuracy further. We describe the procedure involved in evaluating the parser, and obtain accuracies for biomedical data in the same range as those reported for newspaper text, and higher than those previously reported for the biomedical resource on which we evaluate. Our conclusion is that porting newspaper parsers to the biomedical domain, at least for parsers which use lexicalized grammars, may not be as difficult as first thought