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

Looking Beyond the Canonical Formulation and Evaluation Paradigm of Prepositional Phrase Attachment

Prepositional phrase attachment has long been considered one of the most difficult tasks in automated syntactic parsing of natural language text. In this thesis, we examine several aspects of what has become the dominant view of PP attachment in natural language processing with an eye toward extending this view to a more realistic account of the problem. In particular, we take issue with the manner in which most PP attachment work is evaluated, and the degree to which traditional assumptions and simplifications no longer allow for realistically meaningful assessments. We also argue for looking beyond the canonical subset of attachment problems, where almost all attention has been focused, toward a fuller view of the task, both in terms of the types of ambiguities addressed and the contextual information considered

Event extraction from biomedical texts using trimmed dependency graphs

This thesis explores the automatic extraction of information from biomedical publications. Such techniques are urgently needed because the biosciences are publishing continually increasing numbers of texts. The focus of this work is on events. Information about events is currently manually curated from the literature by biocurators. Biocuration, however, is time-consuming and costly so automatic methods are needed for information extraction from the literature. This thesis is dedicated to modeling, implementing and evaluating an advanced event extraction approach based on the analysis of syntactic dependency graphs. This work presents the event extraction approach proposed and its implementation, the JReX (Jena Relation eXtraction) system. This system was used by the University of Jena (JULIE Lab) team in the "BioNLP 2009 Shared Task on Event Extraction" competition and was ranked second among 24 competing teams. Thereafter JReX was the highest scorer on the worldwide shared U-Compare event extraction server, outperforming the competing systems from the challenge. This success was made possible, among other things, by extensive research on event extraction solutions carried out during this thesis, e.g., exploring the effects of syntactic and semantic processing procedures on solving the event extraction task. The evaluations executed on standard and community-wide accepted competition data were complemented by real-life evaluation of large-scale biomedical database reconstruction. This work showed that considerable parts of manually curated databases can be automatically re-created with the help of the event extraction approach developed. Successful re-creation was possible for parts of RegulonDB, the world's largest database for E. coli. In summary, the event extraction approach justified, developed and implemented in this thesis meets the needs of a large community of human curators and thus helps in the acquisition of new knowledge in the biosciences

Embedding Predications

Written communication is rarely a sequence of simple assertions. More often, in addition to simple assertions, authors express subjectivity, such as beliefs, speculations, opinions, intentions, and desires. Furthermore, they link statements of various kinds to form a coherent discourse that reflects their pragmatic intent. In computational semantics, extraction of simple assertions (propositional meaning) has attracted the greatest attention, while research that focuses on extra-propositional aspects of meaning has remained sparse overall and has been largely limited to narrowly defined categories, such as hedging or sentiment analysis, treated in isolation. In this thesis, we contribute to the understanding of extra-propositional meaning in natural language understanding, by providing a comprehensive account of the semantic phenomena that occur beyond simple assertions and examining how a coherent discourse is formed from lower level semantic elements. Our approach is linguistically based, and we propose a general, unified treatment of the semantic phenomena involved, within a computationally viable framework. We identify semantic embedding as the core notion involved in expressing extra-propositional meaning. The embedding framework is based on the structural distinction between embedding and atomic predications, the former corresponding to extra-propositional aspects of meaning. It incorporates the notions of predication source, modality scale, and scope. We develop an embedding categorization scheme and a dictionary based on it, which provide the necessary means to interpret extra-propositional meaning with a compositional semantic interpretation methodology. Our syntax-driven methodology exploits syntactic dependencies to construct a semantic embedding graph of a document. Traversing the graph in a bottom-up manner guided by compositional operations, we construct predications corresponding to extra-propositional semantic content, which form the basis for addressing practical tasks. We focus on text from two distinct domains: news articles from the Wall Street Journal, and scientific articles focusing on molecular biology. Adopting a task-based evaluation strategy, we consider the easy adaptability of the core framework to practical tasks that involve some extra-propositional aspect as a measure of its success. The computational tasks we consider include hedge/uncertainty detection, scope resolution, negation detection, biological event extraction, and attribution resolution. Our competitive results in these tasks demonstrate the viability of our proposal

Un environnement générique et ouvert pour le traitement des expressions polylexicales

The treatment of multiword expressions (MWEs), like take off, bus stop and big deal, is a challenge for NLP applications. This kind of linguistic construction is not only arbitrary but also much more frequent than one would initially guess. This thesis investigates the behaviour of MWEs across different languages, domains and construction types, proposing and evaluating an integrated methodological framework for their acquisition. There have been many theoretical proposals to define, characterise and classify MWEs. We adopt generic definition stating that MWEs are word combinations which must be treated as a unit at some level of linguistic processing. They present a variable degree of institutionalisation, arbitrariness, heterogeneity and limited syntactic and semantic variability. There has been much research on automatic MWE acquisition in the recent decades, and the state of the art covers a large number of techniques and languages. Other tasks involving MWEs, namely disambiguation, interpretation, representation and applications, have received less emphasis in the field. The first main contribution of this thesis is the proposal of an original methodological framework for automatic MWE acquisition from monolingual corpora. This framework is generic, language independent, integrated and contains a freely available implementation, the mwetoolkit. It is composed of independent modules which may themselves use multiple techniques to solve a specific sub-task in MWE acquisition. The evaluation of MWE acquisition is modelled using four independent axes. We underline that the evaluation results depend on parameters of the acquisition context, e.g., nature and size of corpora, language and type of MWE, analysis depth, and existing resources. The second main contribution of this thesis is the application-oriented evaluation of our methodology proposal in two applications: computer-assisted lexicography and statistical machine translation. For the former, we evaluate the usefulness of automatic MWE acquisition with the mwetoolkit for creating three lexicons: Greek nominal expressions, Portuguese complex predicates and Portuguese sentiment expressions. For the latter, we test several integration strategies in order to improve the treatment given to English phrasal verbs when translated by a standard statistical MT system into Portuguese. Both applications can benefit from automatic MWE acquisition, as the expressions acquired automatically from corpora can both speed up and improve the quality of the results. The promising results of previous and ongoing experiments encourage further investigation about the optimal way to integrate MWE treatment into other applications. Thus, we conclude the thesis with an overview of the past, ongoing and future work

Investigating Genotype-Phenotype relationship extraction from biomedical text

During the last decade biomedicine has developed at a tremendous pace. Every day a lot of biomedical papers are published and a large amount of new information is produced. To help enable automated and human interaction in the multitude of applications of this biomedical data, the need for Natural Language Processing systems to process the vast amount of new information is increasing. Our main purpose in this research project is to extract the relationships between genotypes and phenotypes mentioned in the biomedical publications. Such a system provides important and up-to-date data for database construction and updating, and even text summarization. To achieve this goal we had to solve three main problems: finding genotype names, finding phenotype names, and finally extracting phenotype--genotype interactions. We consider all these required modules in a comprehensive system and propose a promising solution for each of them taking into account available tools and resources. BANNER, an open source biomedical named entity recognition system, which has achieved good results in detecting genotypes, has been used for the genotype name recognition task. We were the first group to start working on phenotype name recognition. We have developed two different systems (rule-based and machine-learning based) for extracting phenotype names from text. These systems incorporated the available knowledge from the Unified Medical Language System metathesaurus and the Human Phenotype Onotolgy (HPO). As there was no available annotated corpus for phenotype names, we created a valuable corpus with annotated phenotype names using information available in HPO and a self-training method which can be used for future research. To solve the final problem of this project i.e. , phenotype--genotype relationship extraction, a machine learning method has been proposed. As there was no corpus available for this task and it was not possible for us to annotate a sufficiently large corpus manually, a semi-automatic approach has been used to annotate a small corpus and a self-training method has been proposed to annotate more sentences and enlarge this corpus. A test set was manually annotated by an expert. In addition to having phenotype-genotype relationships annotated, the test set contains important comments about the nature of these relationships. The evaluation results related to each system demonstrate the significantly good performance of all the proposed methods

A modular, open-source information extraction framework for identifying clinical concepts and processes of care in clinical narratives

In this thesis, a synthesis is presented of the knowledge models required by clinical informa- tion systems that provide decision support for longitudinal processes of care. Qualitative research techniques and thematic analysis are novelly applied to a systematic review of the literature on the challenges in implementing such systems, leading to the development of an original conceptual framework. The thesis demonstrates how these process-oriented systems make use of a knowledge base derived from workflow models and clinical guidelines, and argues that one of the major barriers to implementation is the need to extract explicit and implicit information from diverse resources in order to construct the knowledge base. Moreover, concepts in both the knowledge base and in the electronic health record (EHR) must be mapped to a common ontological model. However, the majority of clinical guideline information remains in text form, and much of the useful clinical information residing in the EHR resides in the free text fields of progress notes and laboratory reports. In this thesis, it is shown how natural language processing and information extraction techniques provide a means to identify and formalise the knowledge components required by the knowledge base. Original contributions are made in the development of lexico-syntactic patterns and the use of external domain knowledge resources to tackle a variety of information extraction tasks in the clinical domain, such as recognition of clinical concepts, events, temporal relations, term disambiguation and abbreviation expansion. Methods are developed for adapting existing tools and resources in the biomedical domain to the processing of clinical texts, and approaches to improving the scalability of these tools are proposed and evalu- ated. These tools and techniques are then combined in the creation of a novel approach to identifying processes of care in the clinical narrative. It is demonstrated that resolution of coreferential and anaphoric relations as narratively and temporally ordered chains provides a means to extract linked narrative events and processes of care from clinical notes. Coreference performance in discharge summaries and progress notes is largely dependent on correct identification of protagonist chains (patient, clinician, family relation), pronominal resolution, and string matching that takes account of experiencer, temporal, spatial, and anatomical context; whereas for laboratory reports additional, external domain knowledge is required. The types of external knowledge and their effects on system performance are identified and evaluated. Results are compared against existing systems for solving these tasks and are found to improve on them, or to approach the performance of recently reported, state-of-the- art systems. Software artefacts developed in this research have been made available as open-source components within the General Architecture for Text Engineering framework