Improving treebank-based automatic LFG induction for Spanish

We describe several improvements to the method of treebank-based LFG induction for Spanish from the Cast3LB treebank (O’Donovan et al., 2005). We discuss the different categories of problems encountered and present the solutions adopted. Some of the problems involve a simple adoption of existing linguistic analyses, as in our treatment of clitic doubling and null subjects. In other cases there is no standard LFG account for the phenomenon we wish to model and we adopt a compromise, conservative solution. This is exemplified by our treatment of Spanish periphrastic constructions. In yet another case, the less configurational nature of Spanish means that the LFG annotation algorithm has to rely mostly on Cast3LB function tags, and consequently a reliable method of adding those tags to parse trees had to be developed. This method achieves over 6% improvement over the baseline for the Cast3LB-function-tag assignment task, and over 3% improvement over the baseline for LFG f-structure construction from function-tag-enriched trees

Towards a machine-learning architecture for lexical functional grammar parsing

Data-driven grammar induction aims at producing wide-coverage grammars of human languages. Initial efforts in this field produced relatively shallow linguistic representations such as phrase-structure trees, which only encode constituent structure. Recent work on inducing deep grammars from treebanks addresses this shortcoming by also recovering non-local dependencies and grammatical relations. My aim is to investigate the issues arising when adapting an existing Lexical Functional Grammar (LFG) induction method to a new language and treebank, and find solutions which will generalize robustly across multiple languages. The research hypothesis is that by exploiting machine-learning algorithms to learn morphological features, lemmatization classes and grammatical functions from treebanks we can reduce the amount of manual specification and improve robustness, accuracy and domain- and language -independence for LFG parsing systems. Function labels can often be relatively straightforwardly mapped to LFG grammatical functions. Learning them reliably permits grammar induction to depend less on language-specific LFG annotation rules. I therefore propose ways to improve acquisition of function labels from treebanks and translate those improvements into better-quality f-structure parsing. In a lexicalized grammatical formalism such as LFG a large amount of syntactically relevant information comes from lexical entries. It is, therefore, important to be able to perform morphological analysis in an accurate and robust way for morphologically rich languages. I propose a fully data-driven supervised method to simultaneously lemmatize and morphologically analyze text and obtain competitive or improved results on a range of typologically diverse languages

A Universal Part-of-Speech Tagset

To facilitate future research in unsupervised induction of syntactic structure and to standardize best-practices, we propose a tagset that consists of twelve universal part-of-speech categories. In addition to the tagset, we develop a mapping from 25 different treebank tagsets to this universal set. As a result, when combined with the original treebank data, this universal tagset and mapping produce a dataset consisting of common parts-of-speech for 22 different languages. We highlight the use of this resource via two experiments, including one that reports competitive accuracies for unsupervised grammar induction without gold standard part-of-speech tags

Pronominal anaphora in Basque: annotation of a real corpus

This paper describes the process followed in the annotation of pronominal anaphora in the Eus3LB corpus1 of Basque. Our aim is to use this annotation as the basis for later computational treatment of our language. We present the linguistic analysis carried out, the criteria defined for the tagging and some relevant linguistic conclusions about the features of the antecedents needed to link them correctly to their anaphoric elements

Dependencias Universales para los treebanks AnCora

The present article describes the conversion of the Catalan and Spanish AnCora treebanks to the Universal Dependencies formalism. We describe the conversion process and assess the quality of the resulting treebank in terms of parsing accuracy by means of monolingual, cross-lingual and cross-domain parsing evaluation. The converted treebanks show an internal consistency comparable to the one shown by the original CoNLL09 distribution of AnCora, and indicate some differences in terms of multiword expression inventory with regards to the already existing UD Spanish treebank. The two new converted treebanks will be released in version 1.3 of Universal Dependencies.Este artículo presenta la conversión de los treebanks AnCora del catalán y el castellano al formalismo de Dependencias Universales (UD). Describimos el proceso de conversión y estimamos la calidad de los treebanks resultantes en términos de sus resultados en análisis sintáctico automático en un esquema monolingüe, en un esquema trans-lingüístico y en un tercero trans-dominio. Los treebanks convertidos muestran un nivel de consistencia interna de anotación comparable a la de los datos originales de la distribución CoNLL09 de AnCora, e indican algunas diferencias en términos del inventario de expresiones polilexemáticas con respecto al anterior treebank del castellano en UD. Los dos nuevos treebanks convertidos serán distribuidos con la versión 1.3 de Dependencias Universales

Metrical Annotation of a Large Corpus of Spanish Sonnets: Representation, Scansion and Evaluation

In order to analyze metrical and semantics aspects of poetry in Spanish with computational techniques, we have developed a large corpus annotated with metrical information. In this paper we will present and discuss the development of this corpus: the formal representation of metrical patterns, the semi-automatic annotation process based on a new automatic scansion system, the main annotation problems, and the evaluation, in which an inter-annotator agreement of 96% has been obtained. The corpus is open and available

Automatic extraction of large-scale multilingual lexical resources

In this thesis, I present a methodology for treebank- or parser-based acquisition of lexical resources, in particular sub categorisation frames. The method uses an automatic Lexical Functional Grammar (LFG) f-structure annotation algorithm (Cahill et al., 2002a, 2004a; Burke et al., 2004b) and has been applied to the Penn-II and Penn-III treebanks (Marcus et al., 1994) with a total of about 1.3 million words as well as to (a subset of) the British National Corpus (Bernard, 2002) with about 90 million words. I extract abstract syntactic function-based subcategorisation frames (LFG semantic forms), traditional CFG category-based subcategorisation frames as well as mixed function/category-based frames, with or without preposition information for obliques and particle information for subcategorised particles. The approach distinguishes between active and passive frames, and reflects the effects of long-distance dependencies (LDDs) in the source d ata structures. Frames are associated with conditional probabilities, facilitating the optimisation of the extracted lexicon for quality or coverage through filtering. In contrast to many other approaches, subcategorisation frame types are not predefined but acquired from the source data. I carried out large-scale evaluations of the complete set of forms extracted against the COMLEX and OALD resources. To my knowledge, this is the largest and most complete evaluation of subcategorisation frames for English. The parser-based system is also evaluated against Korhonen (2002) with a statistically significant improvement over the previous best score. The automatic annotation methodology, as well as the grammar and lexicon extraction techniques for English have been successfully migrated to Spanish, German and Chinese treebanks despite typological differences and variations in treebank encoding. I believe that this approach provides an attractive and efficient multilingual grammar and lexicon development paradigm

Sparse Coding of Neural Word Embeddings for Multilingual Sequence Labeling

In this paper we propose and carefully evaluate a sequence labeling framework which solely utilizes sparse indicator features derived from dense distributed word representations. The proposed model obtains (near) state-of-the art performance for both part-of-speech tagging and named entity recognition for a variety of languages. Our model relies only on a few thousand sparse coding-derived features, without applying any modification of the word representations employed for the different tasks. The proposed model has favorable generalization properties as it retains over 89.8% of its average POS tagging accuracy when trained at 1.2% of the total available training data, i.e.~150 sentences per language