Evaluating evaluation measures

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 T¨uBa-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

How to compare treebanks

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

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

Statistical parsing of morphologically rich languages (SPMRL): what, how and whither

The term Morphologically Rich Languages (MRLs) refers to languages in which significant information concerning syntactic units and relations is expressed at word-level. There is ample evidence that the application of readily available statistical parsing models to such languages is susceptible to serious performance degradation. The first workshop on statistical parsing of MRLs hosts a variety of contributions which show that despite language-specific idiosyncrasies, the problems associated with parsing MRLs cut across languages and parsing frameworks. In this paper we review the current state-of-affairs with respect to parsing MRLs and point out central challenges. We synthesize the contributions of researchers working on parsing Arabic, Basque, French, German, Hebrew, Hindi and Korean to point out shared solutions across languages. The overarching analysis suggests itself as a source of directions for future investigations

Why is it so difficult to compare treebanks? TIGER and TüBa-D/Z revisited

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

Evaluation in natural language processing

quot; European Summer School on Language Logic and Information(ESSLLI 2007)(Trinity College Dublin Ireland 6-17 August 2007

Judging grammaticality: experiments in sentence classification

A classifier which is capable of distinguishing a syntactically well formed sentence from a syntactically ill formed one has the potential to be useful in an L2 language-learning context. In this article, we describe a classifier which classifies English sentences as either well formed or ill formed using information gleaned from three different natural language processing techniques. We describe the issues involved in acquiring data to train such a classifier and present experimental results for this classifier on a variety of ill formed sentences. We demonstrate that (a) the combination of information from a variety of linguistic sources is helpful, (b) the trade-off between accuracy on well formed sentences and accuracy on ill formed sentences can be fine tuned by training multiple classifiers in a voting scheme, and (c) the performance of the classifier is varied, with better performance on transcribed spoken sentences produced by less advanced language learners

Benchmarking natural-language parsers for biological applications using dependency graphs

BACKGROUND: Interest is growing in the application of syntactic parsers to natural language processing problems in biology, but assessing their performance is difficult because differences in linguistic convention can falsely appear to be errors. We present a method for evaluating their accuracy using an intermediate representation based on dependency graphs, in which the semantic relationships important in most information extraction tasks are closer to the surface. We also demonstrate how this method can be easily tailored to various application-driven criteria. RESULTS: Using the GENIA corpus as a gold standard, we tested four open-source parsers which have been used in bioinformatics projects. We first present overall performance measures, and test the two leading tools, the Charniak-Lease and Bikel parsers, on subtasks tailored to reflect the requirements of a system for extracting gene expression relationships. These two tools clearly outperform the other parsers in the evaluation, and achieve accuracy levels comparable to or exceeding native dependency parsers on similar tasks in previous biological evaluations. CONCLUSION: Evaluating using dependency graphs allows parsers to be tested easily on criteria chosen according to the semantics of particular biological applications, drawing attention to important mistakes and soaking up many insignificant differences that would otherwise be reported as errors. Generating high-accuracy dependency graphs from the output of phrase-structure parsers also provides access to the more detailed syntax trees that are used in several natural-language processing techniques

Overview of the SPMRL 2013 shared task: cross-framework evaluation of parsing morphologically rich languages

This paper reports on the first shared task on statistical parsing of morphologically rich languages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the evaluation metrics for parsing MRLs given different representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios