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The annotation of parts of speech (POS) in linguistically annotated corpora is a fundamental annotation layer which provides the basis for further syntactic analyses, and many NLP tools rely on POS information as input. However, most POS annotation schemes have been developed with written (newspaper) text in mind and thus do not carry over well to text from other domains and genres. Recent discussions have concentrated on the shortcomings of present POS annotation schemes with regard to their applicability to data from domains other than newspaper text.
This paper discusses the behaviour of German particle verbs formed by two-way prepositions in combination with pleonastic PPs including the verb particle as a preposition. These particle verbs have a characteristic feature: some of them license directional prepositional phrases in the accusative, some only allow for locative PPs in the dative, and some particle verbs can occur with PPs in the accusative and in the dative. Directional particle verbs together with directional PPs present an additional problem: the particle and the preposition in the PP seem to provide redundant information. The paper gives an overview of the semantic verb classes influencing this phenomenon, based on corpus data, and explains the underlying reasons for the behaviour of the particle verbs. We also show how the restrictions on particle verbs and pleonastic PPs can be expressed in a grammar theory like Lexical Functional Grammar (LFG).
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.
In the NLP literature, adapting a parser to new text with properties different from the training data is commonly referred to as domain adaptation. In practice, however, the differences between texts from different sources often reflect a mixture of domain and genre properties, and it is by no means clear what impact each of those has on statistical parsing. In this paper, we investigate how differences between articles in a newspaper corpus relate to the concepts of genre and domain and how they influence parsing performance of a transition-based dependency parser. We do this by applying various similarity measures for data point selection and testing their adequacy for creating genre-aware parsing models.
To improve grammatical function labelling for German, we augment the labelling component of a neural dependency parser with a decision history. We present different ways to encode the history, using different LSTM architectures, and show that our models yield significant improvements, resulting in a LAS for German that is close to the best result from the SPMRL 2014 shared task (without the reranker).
This paper is a contribution to the ongoing discussion on treebank annotation schemes and their impact on PCFG parsing results. We provide a thorough comparison of two German treebanks: the TIGER treebank and the TüBa-D/Z. We use simple statistics on sentence length and vocabulary size, and more refined methods such as perplexity and its correlation with PCFG parsing results, as well as a Principal Components Analysis. Finally we present a qualitative evaluation of a set of 100 sentences from the TüBa- D/Z, manually annotated in the TIGER as well as in the TüBa-D/Z annotation scheme, and show that even the existence of a parallel subcorpus does not support a straightforward and easy comparison of both annotation schemes.
This paper contributes to the discussion on best practices for the syntactic analysis of non-canonical language, focusing on Twitter microtext. We present an annotation experiment where we test an existing POS tagset, the Stuttgart-Tübingen Tagset (STTS), with respect to its applicability for annotating new text from the social media, in particular from Twitter microblogs. We discuss different tagset extensions proposed in the literature and test our extended tagset on a set of 506 tweets (7.418 tokens) where we achieve an inter-annotator agreement for two human annotators in the range of 92.7 to 94.4 (k). Our error analysis shows that especially the annotation of Twitterspecific phenomena such as hashtags and at-mentions causes disagreements between the human annotators. Following up on this, we provide a discussion of the different uses of the @- and #-marker in Twitter and argue against analysing both on the POS level by means of an at-mention or hashtag label. Instead, we sketch a syntactic analysis which describes these phenomena by means of syntactic categories and grammatical functions.
How to Compare Treebanks
(2008)
Recent years have seen an increasing interest in developing standards for linguistic annotation, with a focus on the interoperability of the resources. This effort, however, requires a profound knowledge of the advantages and disadvantages of linguistic annotation schemes in order to avoid importing the flaws and weaknesses of existing encoding schemes into the new standards. This paper addresses the question how to compare syntactically annotated corpora and gain insights into the usefulness of specific design decisions. We present an exhaustive evaluation of two German treebanks with crucially different encoding schemes. We evaluate three different parsers trained on the two treebanks and compare results using EVALB, the Leaf-Ancestor metric, and a dependency-based evaluation. Furthermore, we present TePaCoC, a new testsuite for the evaluation of parsers on complex German grammatical constructions. The testsuite provides a well thought-out error classification, which enables us to compare parser output for parsers trained on treebanks with different encoding schemes and provides interesting insights into the impact of treebank annotation schemes on specific constructions like PP attachment or non-constituent coordination.
Recent studies focussed on the question whether less-configurational languages like German are harder to parse than English, or whether the lower parsing scores are an artefact of treebank encoding schemes and data structures, as claimed by Kübler et al. (2006). This claim is based on the assumption that PARSEVAL metrics fully reflect parse quality across treebank encoding schemes. In this paper we present new experiments to test this claim. We use the PARSEVAL metric, the Leaf-Ancestor metric as well as a dependency-based evaluation, and present novel approaches measuring the effect of controlled error insertion on treebank trees and parser output. We also provide extensive past-parsing crosstreebank conversion. The results of the experiments show that, contrary to Kübler et al. (2006), the question whether or not German is harder to parse than English remains undecided.
This paper presents a thorough examination of the validity of three evaluation measures on parser output. We assess parser performance of an unlexicalised probabilistic parser trained on two German treebanks with different annotation schemes and evaluate parsing results using the PARSEVAL metric, the Leaf-Ancestor metric and a dependency-based evaluation. We reject the claim that the TüBa-D/Z annotation scheme is more adequate then the TIGER scheme for PCFG parsing and show that PARSEVAL should not be used to compare parser performance for parsers trained on treebanks with different annotation schemes. An analysis of specific error types indicates that the dependency-based evaluation is most appropriate to reflect parse quality.