The Scope and the Sources of Variation in Verbal Predicates in English and French

Proceedings of the Ninth International Workshop on Treebanks and Linguistic Theories. Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. NEALT Proceedings Series, Vol. 9 (2010), 199-210. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15891

Nominalization and Alternations in Biomedical Language

Background: This paper presents data on alternations in the argument structure of common domain-specific verbs and their associated verbal nominalizations in the PennBioIE corpus. Alternation is the term in theoretical linguistics for variations in the surface syntactic form of verbs, e.g. the different forms of stimulate in FSH stimulates follicular development and follicular development is stimulated by FSH. The data is used to assess the implications of alternations for biomedical text mining systems and to test the fit of the sublanguage model to biomedical texts. Methodology/Principal Findings: We examined 1,872 tokens of the ten most common domain-specific verbs or their zerorelated nouns in the PennBioIE corpus and labelled them for the presence or absence of three alternations. We then annotated the arguments of 746 tokens of the nominalizations related to these verbs and counted alternations related to the presence or absence of arguments and to the syntactic position of non-absent arguments. We found that alternations are quite common both for verbs and for nominalizations. We also found a previously undescribed alternation involving an adjectival present participle. Conclusions/Significance: We found that even in this semantically restricted domain, alternations are quite common, and alternations involving nominalizations are exceptionally diverse. Nonetheless, the sublanguage model applies to biomedica

Proceedings

Proceedings of the Ninth International Workshop on Treebanks and Linguistic Theories. Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. NEALT Proceedings Series, Vol. 9 (2010), 268 pages. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15891

Adapting Semantic Role Labeling to New Genres and Languages

Semantic role labeling (SRL) is the identification of semantic predicates and their participants within a sentence, which is vital for deeper natural language understanding. State-of-the-art SRL models require annotated text for training, but those annotations don't exist for many languages and domains. The ability to annotate new corpora is hampered by limited time and budget. We explore two different ways of reducing the annotation required to produce SRL systems for new domains or languages: active learning and annotation projection. Active learning reduces annotation requirements by selecting just the most informative training instances through an iterative process of training and annotation. In this work, we investigate the use of Bayesian Active Learning by Disagreement, ways of tuning it for SRL, and assessing its performance across multiple corpora. We study the choices being made by different selection methods over the course of iterations, examining vocabulary coverage, diversity, predicates selected, and the shifts in confidence. We also explore the impact of various strategies of selecting the initial training data. We investigate a number of potentially influential factors within batches of queries, such as diversity and disagreement scores. In order to reduce the overhead of training time, we additionally compare the effect of increasing the amount of queries being selected on each iteration. Abstract Meaning Representations (AMRs) are increasingly popular semantic representations of whole sentences. Based on our successful results using active learning to assess the informativeness of annotation instances for SRL, we look into whether the commonalities between these representations can be leveraged to supply targeted annotation for AMR parsing. Finally, we explore annotation projection of SRL. This approach attempts to create semantic annotations in a target language given parallel translations that have been given SRL annotations through manual or automatic means. We assess the recently developed Russian PropBank and the feasibility of generating the same semantic annotations by projecting from the English PropBank annotation. We use both our own system with English-Russian automatic word alignments and the recent Universal PropBanks 2.0. We examine the types of errors that arise from inconsistencies or gaps in annotations as well as systemic issues arising from the strong English-bias of the projections. This analysis leads us to the development of several filtering techniques that improve the precision of the projections.</p

Investigating the cross-lingual translatability of VerbNet-style classification.

VerbNet-the most extensive online verb lexicon currently available for English-has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, revealing a promising degree of overlap in the resultant classifications. The results on other languages are equally promising. This demonstrates that VerbNet classes have strong cross-lingual potential and the proposed method could be applied to obtain gold standards for automatic verb classification in different languages. We make our annotation guidelines and the six language-specific verb classifications available with this paper

An exploratory study using the predicate-argument structure to develop methodology for measuring semantic similarity of radiology sentences

Indiana University-Purdue University Indianapolis (IUPUI)The amount of information produced in the form of electronic free text in healthcare is increasing to levels incapable of being processed by humans for advancement of his/her professional practice. Information extraction (IE) is a sub-field of natural language processing with the goal of data reduction of unstructured free text. Pertinent to IE is an annotated corpus that frames how IE methods should create a logical expression necessary for processing meaning of text. Most annotation approaches seek to maximize meaning and knowledge by chunking sentences into phrases and mapping these phrases to a knowledge source to create a logical expression. However, these studies consistently have problems addressing semantics and none have addressed the issue of semantic similarity (or synonymy) to achieve data reduction. To achieve data reduction, a successful methodology for data reduction is dependent on a framework that can represent currently popular phrasal methods of IE but also fully represent the sentence. This study explores and reports on the benefits, problems, and requirements to using the predicate-argument statement (PAS) as the framework. A convenient sample from a prior study with ten synsets of 100 unique sentences from radiology reports deemed by domain experts to mean the same thing will be the text from which PAS structures are formed