Proceedings

Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora AEPC 2010. Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk. NEALT Proceedings Series, Vol. 10 (2010), 98 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/10062/15893

Synthetic Treebanking for Cross-Lingual Dependency Parsing

accepted to appear in the special issue on Cross-Language Algorithms and ApplicationsPeer reviewe

Cross-Lingual Transfer of Natural Language Processing Systems

Accurate natural language processing systems rely heavily on annotated datasets. In the absence of such datasets, transfer methods can help to develop a model by transferring annotations from one or more rich-resource languages to the target language of interest. These methods are generally divided into two approaches: 1) annotation projection from translation data, aka parallel data, using supervised models in rich-resource languages, and 2) direct model transfer from annotated datasets in rich-resource languages. In this thesis, we demonstrate different methods for transfer of dependency parsers and sentiment analysis systems. We propose an annotation projection method that performs well in the scenarios for which a large amount of in-domain parallel data is available. We also propose a method which is a combination of annotation projection and direct transfer that can leverage a minimal amount of information from a small out-of-domain parallel dataset to develop highly accurate transfer models. Furthermore, we propose an unsupervised syntactic reordering model to improve the accuracy of dependency parser transfer for non-European languages. Finally, we conduct a diverse set of experiments for the transfer of sentiment analysis systems in different data settings. A summary of our contributions are as follows: * We develop accurate dependency parsers using parallel text in an annotation projection framework. We make use of the fact that the density of word alignments is a valuable indicator of reliability in annotation projection. * We develop accurate dependency parsers in the absence of a large amount of parallel data. We use the Bible data, which is in orders of magnitude smaller than a conventional parallel dataset, to provide minimal cues for creating cross-lingual word representations. Our model is also capable of boosting the performance of annotation projection with a large amount of parallel data. Our model develops cross-lingual word representations for going beyond the traditional delexicalized direct transfer methods. Moreover, we propose a simple but effective word translation approach that brings in explicit lexical features from the target language in our direct transfer method. * We develop different syntactic reordering models that can change the source treebanks in rich-resource languages, thus preventing learning a wrong model for a non-related language. Our experimental results show substantial improvements over non-European languages. * We develop transfer methods for sentiment analysis in different data availability scenarios. We show that we can leverage cross-lingual word embeddings to create accurate sentiment analysis systems in the absence of annotated data in the target language of interest. We believe that the novelties that we introduce in this thesis indicate the usefulness of transfer methods. This is appealing in practice, especially since we suggest eliminating the requirement for annotating new datasets for low-resource languages which is expensive, if not impossible, to obtain

Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision

Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language

D4.1. Technologies and tools for corpus creation, normalization and annotation

The objectives of the Corpus Acquisition and Annotation (CAA) subsystem are the acquisition and processing of monolingual and bilingual language resources (LRs) required in the PANACEA context. Therefore, the CAA subsystem includes: i) a Corpus Acquisition Component (CAC) for extracting monolingual and bilingual data from the web, ii) a component for cleanup and normalization (CNC) of these data and iii) a text processing component (TPC) which consists of NLP tools including modules for sentence splitting, POS tagging, lemmatization, parsing and named entity recognition

Treebank-based grammar acquisition for German

Manual development of deep linguistic resources is time-consuming and costly and therefore often described as a bottleneck for traditional rule-based NLP. In my PhD thesis I present a treebank-based method for the automatic acquisition of LFG resources for German. The method automatically creates deep and rich linguistic presentations from labelled data (treebanks) and can be applied to large data sets. My research is based on and substantially extends previous work on automatically acquiring wide-coverage, deep, constraint-based grammatical resources from the English Penn-II treebank (Cahill et al.,2002; Burke et al., 2004; Cahill, 2004). Best results for English show a dependency f-score of 82.73% (Cahill et al., 2008) against the PARC 700 dependency bank, outperforming the best hand-crafted grammar of Kaplan et al. (2004). Preliminary work has been carried out to test the approach on languages other than English, providing proof of concept for the applicability of the method (Cahill et al., 2003; Cahill, 2004; Cahill et al., 2005). While first results have been promising, a number of important research questions have been raised. The original approach presented first in Cahill et al. (2002) is strongly tailored to English and the datastructures provided by the Penn-II treebank (Marcus et al., 1993). English is configurational and rather poor in inflectional forms. German, by contrast, features semi-free word order and a much richer morphology. Furthermore, treebanks for German differ considerably from the Penn-II treebank as regards data structures and encoding schemes underlying the grammar acquisition task. In my thesis I examine the impact of language-specific properties of German as well as linguistically motivated treebank design decisions on PCFG parsing and LFG grammar acquisition. I present experiments investigating the influence of treebank design on PCFG parsing and show which type of representations are useful for the PCFG and LFG grammar acquisition tasks. Furthermore, I present a novel approach to cross-treebank comparison, measuring the effect of controlled error insertion on treebank trees and parser output from different treebanks. I complement the cross-treebank comparison by providing a human evaluation using TePaCoC, a new testsuite for testing parser performance on complex grammatical constructions. Manual evaluation on TePaCoC data provides new insights on the impact of flat vs. hierarchical annotation schemes on data-driven parsing. I present treebank-based LFG acquisition methodologies for two German treebanks. An extensive evaluation along different dimensions complements the investigation and provides valuable insights for the future development of treebanks

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

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

Robust Parsing for Ungrammatical Sentences

Natural Language Processing (NLP) is a research area that specializes in studying computational approaches to human language. However, not all of the natural language sentences are grammatically correct. Sentences that are ungrammatical, awkward, or too casual/colloquial tend to appear in a variety of NLP applications, from product reviews and social media analysis to intelligent language tutors or multilingual processing. In this thesis, we focus on parsing, because it is an essential component of many NLP applications. We investigate in what ways the performances of statistical parsers degrade when dealing with ungrammatical sentences. We also hypothesize that breaking up parse trees from problematic parts prevents NLP applications from degrading due to incorrect syntactic analysis. A parser is robust if it can overlook problems such as grammar mistakes and produce a parse tree that closely resembles the correct analysis for the intended sentence. We develop a robustness evaluation metric and conduct a series of experiments to compare the performances of state-of-the-art parsers on the ungrammatical sentences. The evaluation results show that ungrammatical sentences present challenges for statistical parsers, because the well-formed syntactic trees they produce may not be appropriate for ungrammatical sentences. We also define a new framework for reviewing the parses of ungrammatical sentences and extracting the coherent parts whose syntactic analyses make sense. We call this task parse tree fragmentation. The experimental results suggest that the proposed overall fragmentation framework is a promising way to handle syntactically unusual sentences

New technologies for Old Germanic: resources and research on parallel bibles in Older Continental Western Germanic

We provide an overview of on-going efforts to facilitate the study of older Germanic languages currently pursued at the Goethe-University Frankfurt, Germany. We describe created resources, such as a parallel corpus of Germanic Bibles and a morphosyntactically annotated corpus of Old High German (OHG) and Old Saxon, a lexicon of OHG in XML and a multilingual etymological database. We discuss NLP algorithms operating on this data, and their relevance for research in the Humanities. RDF and Linked Data represent new and promising aspects in our research, currently applied to establish cross-references between etymological dictionaries, infer new information from their symmetric closure and to formalize linguistic annotations in a corpus and grammatical categories in a lexicon in an interoperable way