Boosting the Coverage of a Semantic Lexicon by Automatically Extracted Event Nominalizations

International audienceAn important trend in recent works on lexical semantics has been the development of learning methods capable of extracting semantic information from text corpora. The majority of these methods are based on the distributional hypothesis of meaning and acquire semantic information by identifying distributional patterns in texts. In this article, we present a distributional analysis method for extracting nominalization relations from monolingual corpora. The acquisition method makes use of distributional and morphological information to select nominalization candidates. We explain how the learning is performed on a dependency annotated corpus and describe the nominalization results. Furthermore, we show how these results served to enrich an existing lexical resource, the WOLF (Wordnet Libre du Français). We present the techniques that we developed in order to integrate the new information into WOLF, based on both its structure and content. Finally, we evaluate the validity of the automatically obtained information and the correctness of its integration into the semantic resource. The method proved to be useful for boosting the coverage of WOLF and presents the advantage of filling verbal synsets, which are particularly difficult to handle due to the high level of verbal polysemy

Developing a TT-MCTAG for German with an RCG-based parser

Developing linguistic resources, in particular grammars, is known to be a complex task in itself, because of (amongst others) redundancy and consistency issues. Furthermore some languages can reveal themselves hard to describe because of specific characteristics, e.g. the free word order in German. In this context, we present (i) a framework allowing to describe tree-based grammars, and (ii) an actual fragment of a core multicomponent tree-adjoining grammar with tree tuples (TT-MCTAG) for German developed using this framework. This framework combines a metagrammar compiler and a parser based on range concatenation grammar (RCG) to respectively check the consistency and the correction of the grammar. The German grammar being developed within this framework already deals with a wide range of scrambling and extraction phenomena

Inquiries into the lexicon-syntax relations in Basque

Index:- Foreword. B. Oyharçabal.- Morphosyntactic disambiguation and shallow parsing in computational processing in Basque. I. Aduriz, A. Díaz de Ilarraza.- The transitivity of borrowed verbs in Basque: an outline. X. Alberdi.- Patrixa: a unification-based parser for Basque and its application to the automatic analysis of verbs. I. Aldezabal, M. J. Aranzabe, A. Atutxa, K.Gojenola, K, Sarasola.- Learning argument/adjunct distinction for Basque. I. Aldezabal, M. J. Aranzabe, K. Gojenola, K, Sarasola, A. Atutxa.- Analyzing verbal subcategorization aimed at its computation application. I. Aldezabal, P. Goenaga.- Automatic extraction of verb paterns from “hauta-lanerako euskal hiztegia”. J. M. Arriola, X. Artola, A. Soroa.- The case of an enlightening, provoking an admirable Basque derivational siffux with implications for the theory of argument structure. X. Artiagoitia.- Verb-deriving processes in Basque. J. C. Odriozola.- Lexical causatives and causative alternation in Basque. B. Oyharçabal.- Causation and semantic control; diagnosis of incorrect use in minorized languages. I. Zabala.- Subject index.- Contributions

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

Can humain association norm evaluate latent semantic analysis?

This paper presents the comparison of word association norm created by a psycholinguistic experiment to association lists generated by algorithms operating on text corpora. We compare lists generated by Church and Hanks algorithm and lists generated by LSA algorithm. An argument is presented on how those automatically generated lists reflect real semantic relations

Acquiring and Harnessing Verb Knowledge for Multilingual Natural Language Processing

Advances in representation learning have enabled natural language processing models to derive non-negligible linguistic information directly from text corpora in an unsupervised fashion. However, this signal is underused in downstream tasks, where they tend to fall back on superficial cues and heuristics to solve the problem at hand. Further progress relies on identifying and filling the gaps in linguistic knowledge captured in their parameters. The objective of this thesis is to address these challenges focusing on the issues of resource scarcity, interpretability, and lexical knowledge injection, with an emphasis on the category of verbs. To this end, I propose a novel paradigm for efficient acquisition of lexical knowledge leveraging native speakers’ intuitions about verb meaning to support development and downstream performance of NLP models across languages. First, I investigate the potential of acquiring semantic verb classes from non-experts through manual clustering. This subsequently informs the development of a two-phase semantic dataset creation methodology, which combines semantic clustering with fine-grained semantic similarity judgments collected through spatial arrangements of lexical stimuli. The method is tested on English and then applied to a typologically diverse sample of languages to produce the first large-scale multilingual verb dataset of this kind. I demonstrate its utility as a diagnostic tool by carrying out a comprehensive evaluation of state-of-the-art NLP models, probing representation quality across languages and domains of verb meaning, and shedding light on their deficiencies. Subsequently, I directly address these shortcomings by injecting lexical knowledge into large pretrained language models. I demonstrate that external manually curated information about verbs’ lexical properties can support data-driven models in tasks where accurate verb processing is key. Moreover, I examine the potential of extending these benefits from resource-rich to resource-poor languages through translation-based transfer. The results emphasise the usefulness of human-generated lexical knowledge in supporting NLP models and suggest that time-efficient construction of lexicons similar to those developed in this work, especially in under-resourced languages, can play an important role in boosting their linguistic capacity.ESRC Doctoral Fellowship [ES/J500033/1], ERC Consolidator Grant LEXICAL [648909

Exploring formal models of linguistic data structuring. Enhanced solutions for knowledge management systems based on NLP applications

2010 - 2011The principal aim of this research is describing to which extent formal models for linguistic data structuring are crucial in Natural Language Processing (NLP) applications. In this sense, we will pay particular attention to those Knowledge Management Systems (KMS) which are designed for the Internet, and also to the enhanced solutions they may require. In order to appropriately deal with this topics, we will describe how to achieve computational linguistics applications helpful to humans in establishing and maintaining an advantageous relationship with technologies, especially with those technologies which are based on or produce man-machine interactions in natural language. We will explore the positive relationship which may exist between well-structured Linguistic Resources (LR) and KMS, in order to state that if the information architecture of a KMS is based on the formalization of linguistic data, then the system works better and is more consistent. As for the topics we want to deal with, frist of all it is indispensable to state that in order to structure efficient and effective Information Retrieval (IR) tools, understanding and formalizing natural language combinatory mechanisms seems to be the first operation to achieve, also because any piece of information produced by humans on the Internet is necessarily a linguistic act. Therefore, in this research work we will also discuss the NLP structuring of a linguistic formalization Hybrid Model, which we hope will prove to be a useful tool to support, improve and refine KMSs. More specifically, in section 1 we will describe how to structure language resources implementable inside KMSs, to what extent they can improve the performance of these systems and how the problem of linguistic data structuring is dealt with by natural language formalization methods. In section 2 we will proceed with a brief review of computational linguistics, paying particular attention to specific software packages such Intex, Unitex, NooJ, and Cataloga, which are developed according to Lexicon-Grammar (LG) method, a linguistic theory established during the 60’s by Maurice Gross. In section 3 we will describe some specific works useful to monitor the state of the art in Linguistic Data Structuring Models, Enhanced Solutions for KMSs, and NLP Applications for KMSs. In section 4 we will cope with problems related to natural language formalization methods, describing mainly Transformational-Generative Grammar (TGG) and LG, plus other methods based on statistical approaches and ontologies. In section 5 we will propose a Hybrid Model usable in NLP applications in order to create effective enhanced solutions for KMSs. Specific features and elements of our hybrid model will be shown through some results on experimental research work. The case study we will present is a very complex NLP problem yet little explored in recent years, i.e. Multi Word Units (MWUs) treatment. In section 6 we will close our research evaluating its results and presenting possible future work perspectives. [edited by author]X n.s