Modeling the interface between morphology and syntax in data-driven dependency parsing

When people formulate sentences in a language, they follow a set of rules specific to that language that defines how words must be put together in order to express the intended meaning. These rules are called the grammar of the language. Languages have essentially two ways of encoding grammatical information: word order or word form. English uses primarily word order to encode different meanings, but many other languages change the form of the words themselves to express their grammatical function in the sentence. These languages are commonly subsumed under the term morphologically rich languages. Parsing is the automatic process for predicting the grammatical structure of a sentence. Since grammatical structure guides the way we understand sentences, parsing is a key component in computer programs that try to automatically understand what people say and write. This dissertation is about parsing and specifically about parsing languages with a rich morphology, which encode grammatical information in the form of words. Today’s parsing models for automatic parsing were developed for English and achieve good results on this language. However, when applied to other languages, a significant drop in performance is usually observed. The standard model for parsing is a pipeline model that separates the parsing process into different steps, in particular it separates the morphological analysis, i.e. the analysis of word forms, from the actual parsing step. This dissertation argues that this separation is one of the reasons for the performance drop of standard parsers when applied to other languages than English. An analysis is presented that exposes the connection between the morphological system of a language and the errors of a standard parsing model. In a second series of experiments, we show that knowledge about the syntactic structure of sentence can support the prediction of morphological information. We then argue for an alternative approach that models morphological analysis and syntactic analysis jointly instead of separating them. We support this argumentation with empirical evidence by implementing two parsers that model the relationship between morphology and syntax in two different but complementary ways

First International Workshop on Lexical Resources

International audienceLexical resources are one of the main sources of linguistic information for research and applications in Natural Language Processing and related fields. In recent years advances have been achieved in both symbolic aspects of lexical resource development (lexical formalisms, rule-based tools) and statistical techniques for the acquisition and enrichment of lexical resources, both monolingual and multilingual. The latter have allowed for faster development of large-scale morphological, syntactic and/or semantic resources, for widely-used as well as resource-scarce languages. Moreover, the notion of dynamic lexicon is used increasingly for taking into account the fact that the lexicon undergoes a permanent evolution.This workshop aims at sketching a large picture of the state of the art in the domain of lexical resource modeling and development. It is also dedicated to research on the application of lexical resources for improving corpus-based studies and language processing tools, both in NLP and in other language-related fields, such as linguistics, translation studies, and didactics