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

Parsing word clusters

International audienceWe present and discuss experiments in statistical parsing of French, where terminal forms used during training and parsing are replaced by more general symbols, particularly clusters of words obtained through unsupervised linear clustering. We build on the work of Candito and Crabbé (2009) who proposed to use clusters built over slightly coarsened French inflected forms. We investigate the alternative method of building clusters over lemma/part-of-speech pairs, using a raw corpus automatically tagged and lemmatized. We find that both methods lead to comparable improvement over the baseline (we obtain F_1=86.20% and F_1=86.21% respectively, compared to a baseline of F_1=84.10%). Yet, when we replace gold lemma/POS pairs with their corresponding cluster, we obtain an upper bound (F_1=87.80) that suggests room for improvement for this technique, should tagging/lemmatisation performance increase for French. We also analyze the improvement in performance for both techniques with respect to word frequency. We find that replacing word forms with clusters improves attachment performance for words that are originally either unknown or low-frequency, since these words are replaced by cluster symbols that tend to have higher frequencies. Furthermore, clustering also helps significantly for medium to high frequency words, suggesting that training on word clusters leads to better probability estimates for these words

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

Parsing word clusters

International audienceWe present and discuss experiments in statistical parsing of French, where terminal forms used during training and parsing are replaced by more general symbols, particularly clusters of words obtained through unsupervised linear clustering. We build on the work of Candito and Crabbé (2009) who proposed to use clusters built over slightly coarsened French inflected forms. We investigate the alternative method of building clusters over lemma/part-of-speech pairs, using a raw corpus automatically tagged and lemmatized. We find that both methods lead to comparable improvement over the baseline (we obtain F_1=86.20% and F_1=86.21% respectively, compared to a baseline of F_1=84.10%). Yet, when we replace gold lemma/POS pairs with their corresponding cluster, we obtain an upper bound (F_1=87.80) that suggests room for improvement for this technique, should tagging/lemmatisation performance increase for French. We also analyze the improvement in performance for both techniques with respect to word frequency. We find that replacing word forms with clusters improves attachment performance for words that are originally either unknown or low-frequency, since these words are replaced by cluster symbols that tend to have higher frequencies. Furthermore, clustering also helps significantly for medium to high frequency words, suggesting that training on word clusters leads to better probability estimates for these words

Identifying Causal Relations in Legal Documents with Dependency Syntactic Analysis

This article describes a method for enriching a dependency-based parser with causal connectors. Our specific objective is to identify causal relationships between elementary discourse units in Spanish legal texts. For this purpose, the approach we follow is to search for specific discourse connectives which are taken as causal dependencies relating an effect event (head) with a verbal or nominal cause (dependent). As a result, we turn a specific syntactic parser into a discourse parser aimed at recognizing causal structures

Statistical French dependency parsing: treebank conversion and first results

International audienceWe first describe the automatic conversion of the French Treebank (Abeillé and Barrier, 2004), a constituency treebank, into typed projective dependency trees. In order to evaluate the overall quality of the resulting dependency treebank, and to quantify the cases where the projectivity constraint leads to wrong dependencies, we compare a subset of the converted treebank to manually validated dependency trees. We then compare the performance of two treebank-trained parsers that output typed dependency parses. The first parser is the MST parser (McDonald et al., 2006), which we directly train on dependency trees. The second parser is a combination of the Berkeley parser (Petrov et al., 2006) and a functional role labeler: trained on the original constituency treebank, the Berkeley parser first outputs constituency trees, which are then labeled with functional roles, and then converted into dependency trees. We found that used in combination with a high-accuracy French POS tagger, the MST parser performs a little better for unlabeled dependencies (UAS=90.3% versus 89.6%), and better for labeled dependencies (LAS=87.6% versus 85.6%)