Multilingual Dependency Parsing and Domain Adaptation using DeSR

We describe our experiments using the DeSR parser in the multilingual and domain adaptation tracks of the CoNLL 2007 shared task. DeSR implements an incremental deterministic Shift/Reduce parsing algorithm, using specific rules to handle non-projective dependencies. For the multilingual track we adopted a second order averaged perceptron and performed feature selection to tune a feature model for each language. For the domain adaptation track we applied a tree revision ethod which learns how to correct the mistakes made by the base parser on the adaptation domain

ULISSE: an unsupervised algorithm for detecting reliable dependency parses

In this paper we present ULISSE, an unsupervised linguistically--driven algorithm to select reliable parses from the output of a dependency parser. Different experiments were devised to show that the algorithm is robust enough to deal with the output of different parsers and with different languages, as well as to be used across different domains. In all cases, ULISSE appears to outperform the baseline algorithms

The CoNLL 2007 shared task on dependency parsing

The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learning systems on the same data sets. In 2007, as in 2006, the shared task has been devoted to dependency parsing, this year with both a multilingual track and a domain adaptation track. In this paper, we define the tasks of the different tracks and describe how the data sets were created from existing treebanks for ten languages. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results

Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP

The transfer or share of knowledge between languages is a popular solution to resource scarcity in NLP. However, the effectiveness of cross-lingual transfer can be challenged by variation in syntactic structures. Frameworks such as Universal Dependencies (UD) are designed to be cross-lingually consistent, but even in carefully designed resources trees representing equivalent sentences may not always overlap. In this paper, we measure cross-lingual syntactic variation, or anisomorphism, in the UD treebank collection, considering both morphological and structural properties. We show that reducing the level of anisomorphism yields consistent gains in cross-lingual transfer tasks. We introduce a source language selection procedure that facilitates effective cross-lingual parser transfer, and propose a typologically driven method for syntactic tree processing which reduces anisomorphism. Our results show the effectiveness of this method for both machine translation and cross-lingual sentence similarity, demonstrating the importance of syntactic structure compatibility for boosting cross-lingual transfer in NLP

Domain Adaptation for Dependency Parsing at Evalita 2011

The domain adaptation task was aimed at investigating techniques for adapting state-of-the-art dependency parsing systems to new domains. Both the language dealt with, i.e. Italian, and the target domain, namely the legal domain, represent two main novelties of the task organised at Evalita 2011. In this paper, we define the task and describe how the datasets were created from different resources. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results

Converting Italian Treebanks: Towards an Italian Stanford Dependency Treebank

The paper addresses the challenge of converting MIDT, an existing dependencybased Italian treebank resulting from the harmonization and merging of smaller resources, into the Stanford Dependencies annotation formalism, with the final aim of constructing a standard–compliant resource for the Italian language. Achieved results include a methodology for converting treebank annotations belonging to the same dependency–based family, the Italian Stanford Dependency Treebank (ISDT), and an Italian localization of the Stanford Dependency scheme

Harmonization and Merging of two Italian Dependency Treebanks

The paper describes the methodology which is currently being defined for the construction of a "Merged Italian Dependency Treebank'' (MIDT) starting from already existing resources. In particular, it reports the results of a case study carried out on two available dependency treebanks, i.e. TUT and ISST--TANL. The issues raised during the comparison of the annotation schemes underlying the two treebanks are discussed and investigated with a particular emphasis on the definition of a set of linguistic categories to be used as a "bridge'' between the specific schemes. As an encoding format, the CoNLL de facto standard is used

The PAIS? Corpus of Italian Web Texts

PAISA\u27 is a Creative Commons licensed, large web corpus of contemporary Italian. We describe the design, harvesting, and processing steps involved in its creation

D4.1. Technologies and tools for corpus creation, normalization and annotation

The objectives of the Corpus Acquisition and Annotation (CAA) subsystem are the acquisition and processing of monolingual and bilingual language resources (LRs) required in the PANACEA context. Therefore, the CAA subsystem includes: i) a Corpus Acquisition Component (CAC) for extracting monolingual and bilingual data from the web, ii) a component for cleanup and normalization (CNC) of these data and iii) a text processing component (TPC) which consists of NLP tools including modules for sentence splitting, POS tagging, lemmatization, parsing and named entity recognition

D6.2 Integrated Final Version of the Components for Lexical Acquisition

The PANACEA project has addressed one of the most critical bottlenecks that threaten the development of technologies to support multilingualism in Europe, and to process the huge quantity of multilingual data produced annually. Any attempt at automated language processing, particularly Machine Translation (MT), depends on the availability of language-specific resources. Such Language Resources (LR) contain information about the language\u27s lexicon, i.e. the words of the language and the characteristics of their use. In Natural Language Processing (NLP), LRs contribute information about the syntactic and semantic behaviour of words - i.e. their grammar and their meaning - which inform downstream applications such as MT. To date, many LRs have been generated by hand, requiring significant manual labour from linguistic experts. However, proceeding manually, it is impossible to supply LRs for every possible pair of European languages, textual domain, and genre, which are needed by MT developers. Moreover, an LR for a given language can never be considered complete nor final because of the characteristics of natural language, which continually undergoes changes, especially spurred on by the emergence of new knowledge domains and new technologies. PANACEA has addressed this challenge by building a factory of LRs that progressively automates the stages involved in the acquisition, production, updating and maintenance of LRs required by MT systems. The existence of such a factory will significantly cut down the cost, time and human effort required to build LRs. WP6 has addressed the lexical acquisition component of the LR factory, that is, the techniques for automated extraction of key lexical information from texts, and the automatic collation of lexical information into LRs in a standardized format. The goal of WP6 has been to take existing techniques capable of acquiring syntactic and semantic information from corpus data, improving upon them, adapting and applying them to multiple languages, and turning them into powerful and flexible techniques capable of supporting massive applications. One focus for improving the scalability and portability of lexical acquisition techniques has been to extend exiting techniques with more powerful, less "supervised" methods. In NLP, the amount of supervision refers to the amount of manual annotation which must be applied to a text corpus before machine learning or other techniques are applied to the data to compile a lexicon. More manual annotation means more accurate training data, and thus a more accurate LR. However, given that it is impractical from a cost and time perspective to manually annotate the vast amounts of data required for multilingual MT across domains, it is important to develop techniques which can learn from corpora with less supervision. Less supervised methods are capable of supporting both large-scale acquisition and efficient domain adaptation, even in the domains where data is scarce. Another focus of lexical acquisition in PANACEA has been the need of LR users to tune the accuracy level of LRs. Some applications may require increased precision, or accuracy, where the application requires a high degree of confidence in the lexical information used. At other times a greater level of coverage may be required, with information about more words at the expense of some degree of accuracy. Lexical acquisition in PANACEA has investigated confidence thresholds for lexical acquisition to ensure that the ultimate users of LRs can generate lexical data from the PANACEA factory at the desired level of accuracy