Corpus Annotation for Parser Evaluation

We describe a recently developed corpus annotation scheme for evaluating parsers that avoids shortcomings of current methods. The scheme encodes grammatical relations between heads and dependents, and has been used to mark up a new public-domain corpus of naturally occurring English text. We show how the corpus can be used to evaluate the accuracy of a robust parser, and relate the corpus to extant resources.Comment: 7 pages, LaTeX (uses eaclap.sty

Treebank-based acquisition of a Chinese lexical-functional grammar

Scaling wide-coverage, constraint-based grammars such as Lexical-Functional Grammars (LFG) (Kaplan and Bresnan, 1982; Bresnan, 2001) or Head-Driven Phrase Structure Grammars (HPSG) (Pollard and Sag, 1994) from fragments to naturally occurring unrestricted text is knowledge-intensive, time-consuming and (often prohibitively) expensive. A number of researchers have recently presented methods to automatically acquire wide-coverage, probabilistic constraint-based grammatical resources from treebanks (Cahill et al., 2002, Cahill et al., 2003; Cahill et al., 2004; Miyao et al., 2003; Miyao et al., 2004; Hockenmaier and Steedman, 2002; Hockenmaier, 2003), addressing the knowledge acquisition bottleneck in constraint-based grammar development. Research to date has concentrated on English and German. In this paper we report on an experiment to induce wide-coverage, probabilistic LFG grammatical and lexical resources for Chinese from the Penn Chinese Treebank (CTB) (Xue et al., 2002) based on an automatic f-structure annotation algorithm. Currently 96.751% of the CTB trees receive a single, covering and connected f-structure, 0.112% do not receive an f-structure due to feature clashes, while 3.137% are associated with multiple f-structure fragments. From the f-structure-annotated CTB we extract a total of 12975 lexical entries with 20 distinct subcategorisation frame types. Of these 3436 are verbal entries with a total of 11 different frame types. We extract a number of PCFG-based LFG approximations. Currently our best automatically induced grammars achieve an f-score of 81.57% against the trees in unseen articles 301-325; 86.06% f-score (all grammatical functions) and 73.98% (preds-only) against the dependencies derived from the f-structures automatically generated for the original trees in 301-325 and 82.79% (all grammatical functions) and 67.74% (preds-only) against the dependencies derived from the manually annotated gold-standard f-structures for 50 trees randomly selected from articles 301-325

Reflexive pronouns in Spanish Universal Dependencies

In this paper, we argue that in current Universal Dependencies treebanks, the annotation of Spanish reflexives is an unsolved problem, which clearly affects the accuracy and consistency of current parsers. We evaluate different proposals for fine-tuning the various categories, and discuss remaining open issues. We believe that the solution for these issues could lie in a multi-layered way of annotating the characteristics, combining annotation of the dependency relation and of the so-called token features, rather than in expanding the number of categories on one layer. We apply this proposal to the v2.5 Spanish UD AnCora treebank and provide a categorized conversion table that can be run with a Python script

Automatic treebank-based acquisition of Arabic LFG dependency structures

A number of papers have reported on methods for the automatic acquisition of large-scale, probabilistic LFG-based grammatical resources from treebanks for English (Cahill and al., 2002), (Cahill and al., 2004), German (Cahill and al., 2003), Chinese (Burke, 2004), (Guo and al., 2007), Spanish (O’Donovan, 2004), (Chrupala and van Genabith, 2006) and French (Schluter and van Genabith, 2008). Here, we extend the LFG grammar acquisition approach to Arabic and the Penn Arabic Treebank (ATB) (Maamouri and Bies, 2004), adapting and extending the methodology of (Cahill and al., 2004) originally developed for English. Arabic is challenging because of its morphological richness and syntactic complexity. Currently 98% of ATB trees (without FRAG and X) produce a covering and connected f-structure. We conduct a qualitative evaluation of our annotation against a gold standard and achieve an f-score of 95%

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

New Treebank or Repurposed? On the Feasibility of Cross-Lingual Parsing of Romance Languages with Universal Dependencies

This is the final peer-reviewed manuscript that was accepted for publication in Natural Language Engineering. Changes resulting from the publishing process, such as editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document.[Abstract] This paper addresses the feasibility of cross-lingual parsing with Universal Dependencies (UD) between Romance languages, analyzing its performance when compared to the use of manually annotated resources of the target languages. Several experiments take into account factors such as the lexical distance between the source and target varieties, the impact of delexicalization, the combination of different source treebanks or the adaptation of resources to the target language, among others. The results of these evaluations show that the direct application of a parser from one Romance language to another reaches similar labeled attachment score (LAS) values to those obtained with a manual annotation of about 3,000 tokens in the target language, and unlabeled attachment score (UAS) results equivalent to the use of around 7,000 tokens, depending on the case. These numbers can noticeably increase by performing a focused selection of the source treebanks. Furthermore, the removal of the words in the training corpus (delexicalization) is not useful in most cases of cross-lingual parsing of Romance languages. The lessons learned with the performed experiments were used to build a new UD treebank for Galician, with 1,000 sentences manually corrected after an automatic cross-lingual annotation. Several evaluations in this new resource show that a cross-lingual parser built with the best combination and adaptation of the source treebanks performs better (77 percent LAS and 82 percent UAS) than using more than 16,000 (for LAS results) and more than 20,000 (UAS) manually labeled tokens of Galician.Ministerio de Economía y Competitividad; FJCI-2014-22853Ministerio de Economía y Competitividad; FFI2014-51978-C2-1-RMinisterio de Economía y Competitividad; FFI2014-51978-C2-2-

Treebank-based acquisition of LFG resources for Chinese

This paper presents a method to automatically acquire wide-coverage, robust, probabilistic Lexical-Functional Grammar resources for Chinese from the Penn Chinese Treebank (CTB). Our starting point is the earlier, proofof- concept work of (Burke et al., 2004) on automatic f-structure annotation, LFG grammar acquisition and parsing for Chinese using the CTB version 2 (CTB2). We substantially extend and improve on this earlier research as regards coverage, robustness, quality and fine-grainedness of the resulting LFG resources. We achieve this through (i) improved LFG analyses for a number of core Chinese phenomena; (ii) a new automatic f-structure annotation architecture which involves an intermediate dependency representation; (iii) scaling the approach from 4.1K trees in CTB2 to 18.8K trees in CTB version 5.1 (CTB5.1) and (iv) developing a novel treebank-based approach to recovering non-local dependencies (NLDs) for Chinese parser output. Against a new 200-sentence good standard of manually constructed f-structures, the method achieves 96.00% f-score for f-structures automatically generated for the original CTB trees and 80.01%for NLD-recovered f-structures generated for the trees output by Bikel’s parser

Implementing universal dependency, morphology, and multiword expression annotation standards for Turkish language processing

Released only a year ago as the outputs of a research project (“Parsing Web 2.0 Sentences”, supported in part by a TUBİTAK 1001 grant (No. 112E276) and a part of the ICT COST Action PARSEME (IC1207)), IMST and IWT are currently the most comprehensive Turkish dependency treebanks in the literature. This article introduces the final states of our treebanks, as well as a newly integrated hierarchical categorization of the multiheaded dependencies and their organization in an exclusive deep dependency layer in the treebanks. It also presents the adaptation of recent studies on standardizing multiword expression and named entity annotation schemes for the Turkish language and integration of benchmark annotations into the dependency layers of our treebanks and the mapping of the treebanks to the latest Universal Dependencies (v2.0) standard, ensuring further compliance with rising universal annotation trends. In addition to significantly boosting the universal recognition of Turkish treebanks, our recent efforts have shown an improvement in their syntactic parsing performance (up to 77.8%/82.8% LAS and 84.0%/87.9% UAS for IMST/IWT, respectively). The final states of the treebanks are expected to be more suited to different natural language processing tasks, such as named entity recognition, multiword expression detection, transfer-based machine translation, semantic parsing, and semantic role labeling.Peer reviewe