Using graphical models for PP attachment

Proceedings of the 18th Nordic Conference of Computational Linguistics NODALIDA 2011. Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa. NEALT Proceedings Series, Vol. 11 (2011), 206-213. © 2011 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/10062/16955

Natural Language Processing Resources for Finnish. Corpus Development in the General and Clinical Domains

Siirretty Doriast

A new term weighting method for text categorization

Ph.DDOCTOR OF PHILOSOPH

Text Mining and Gene Expression Analysis Towards Combined Interpretation of High Throughput Data

Microarrays can capture gene expression activity for thousands of genes simultaneously and thus make it possible to analyze cell physiology and disease processes on molecular level. The interpretation of microarray gene expression experiments profits from knowledge on the analyzed genes and proteins and the biochemical networks in which they play a role. The trend is towards the development of data analysis methods that integrate diverse data types. Currently, the most comprehensive biomedical knowledge source is a large repository of free text articles. Text mining makes it possible to automatically extract and use information from texts. This thesis addresses two key aspects, biomedical text mining and gene expression data analysis, with the focus on providing high-quality methods and data that contribute to the development of integrated analysis approaches. The work is structured in three parts. Each part begins by providing the relevant background, and each chapter describes the developed methods as well as applications and results. Part I deals with biomedical text mining: Chapter 2 summarizes the relevant background of text mining; it describes text mining fundamentals, important text mining tasks, applications and particularities of text mining in the biomedical domain, and evaluation issues. In Chapter 3, a method for generating high-quality gene and protein name dictionaries is described. The analysis of the generated dictionaries revealed important properties of individual nomenclatures and the used databases (Fundel and Zimmer, 2006). The dictionaries are publicly available via a Wiki, a web service, and several client applications (Szugat et al., 2005). In Chapter 4, methods for the dictionary-based recognition of gene and protein names in texts and their mapping onto unique database identifiers are described. These methods make it possible to extract information from texts and to integrate text-derived information with data from other sources. Three named entity identification systems have been set up, two of them building upon the previously existing tool ProMiner (Hanisch et al., 2003). All of them have shown very good performance in the BioCreAtIvE challenges (Fundel et al., 2005a; Hanisch et al., 2005; Fundel and Zimmer, 2007). In Chapter 5, a new method for relation extraction (Fundel et al., 2007) is presented. It was applied on the largest collection of biomedical literature abstracts, and thus a comprehensive network of human gene and protein relations has been generated. A classification approach (Küffner et al., 2006) can be used to specify relation types further; e. g., as activating, direct physical, or gene regulatory relation. Part II deals with gene expression data analysis: Gene expression data needs to be processed so that differentially expressed genes can be identified. Gene expression data processing consists of several sequential steps. Two important steps are normalization, which aims at removing systematic variances between measurements, and quantification of differential expression by p-value and fold change determination. Numerous methods exist for these tasks. Chapter 6 describes the relevant background of gene expression data analysis; it presents the biological and technical principles of microarrays and gives an overview of the most relevant data processing steps. Finally, it provides a short introduction to osteoarthritis, which is in the focus of the analyzed gene expression data sets. In Chapter 7, quality criteria for the selection of normalization methods are described, and a method for the identification of differentially expressed genes is proposed, which is appropriate for data with large intensity variances between spots representing the same gene (Fundel et al., 2005b). Furthermore, a system is described that selects an appropriate combination of feature selection method and classifier, and thus identifies genes which lead to good classification results and show consistent behavior in different sample subgroups (Davis et al., 2006). The analysis of several gene expression data sets dealing with osteoarthritis is described in Chapter 8. This chapter contains the biomedical analysis of relevant disease processes and distinct disease stages (Aigner et al., 2006a), and a comparison of various microarray platforms and osteoarthritis models. Part III deals with integrated approaches and thus provides the connection between parts I and II: Chapter 9 gives an overview of different types of integrated data analysis approaches, with a focus on approaches that integrate gene expression data with manually compiled data, large-scale networks, or text mining. In Chapter 10, a method for the identification of genes which are consistently regulated and have a coherent literature background (Küffner et al., 2005) is described. This method indicates how gene and protein name identification and gene expression data can be integrated to return clusters which contain genes that are relevant for the respective experiment together with literature information that supports interpretation. Finally, in Chapter 11 ideas on how the described methods can contribute to current research and possible future directions are presented

Narrative comprehension through analogy: A study in cognitive modeling and narrative clustering

As the field of natural language processing improves and finds its way into everyday use its current limitations and shortcomings become all the more apparent. The next generation of NLP systems will need to be able to handle tasks at a higher level, drawing together information beyond the lexical and across sentence boundaries. To address this need, research into the field of discourse understanding has emerged as a current hot topic with special attention being drawn to narrative comprehension. We explore cognitive modeling and the application of derived measures of analogy to tasks in the discourse/narrative domains. First, we present improvements to the LISA model, a state-of-the-art cognitive model of analogy, increasing the model’s flexibility and robustness, extending the model’s functionality to include a probabilistic measure of belief, and presenting an algorithm for automatically producing the model’s encoding. Finally we test the utility of narrative analogy as a feature for the Story Cloze Task. We find that narrative analogy is a poor feature on its own, but as part of a composite model with sentiment analysis, it outperforms the best task-given baselines but under-performs state-of-the-art. More importantly, through failure analysis we find that narrative analogy, as conceptualized by the field, is insufficient for such tasks, and researchers must first be able to determine when an analogy should be drawn since simply finding all potential analogies proves insufficient

Online learning of latent linguistic structure with approximate search

Automatic analysis of natural language data is a frequently occurring application of machine learning systems. These analyses often revolve around some linguistic structure, for instance a syntactic analysis of a sentence by means of a tree. Machine learning models that carry out structured prediction, as opposed to simpler machine learning tasks such as classification or regression, have therefore received considerable attention in the language processing literature. As an additional twist, the sought linguistic structures are sometimes not directly modeled themselves. Rather, prediction takes place in a different space where the same linguistic structure can be represented in more than one way. However, in a standard supervised learning setting, these prediction structures are not available in the training data, but only the linguistic structure. Since multiple prediction structures may correspond to the same linguistic structure, it is thus unclear which prediction structure to use for learning. One option is to treat the prediction structure as latent and let the machine learning algorithm guide this selection. In this dissertation we present an abstract framework for structured prediction. This framework supports latent structures and is agnostic of the particular language processing task. It defines a set of hyperparameters and task-specific functions which a user must implement in order to apply it to a new task. The advantage of this modularization is that it permits comparisons and reuse across tasks in a common framework. The framework we devise is based on the structured perceptron for learning. The perceptron is an online learning algorithm which considers one training instance at a time, makes a prediction, and carries out an update if the prediction was wrong. We couple the structured perceptron with beam search, which is a general purpose search algorithm. Beam search is, however, only approximate, meaning that there is no guarantee that it will find the optimal structure in a large search space. Therefore special attention is required to handle search errors during training. This has led to the development of special update methods such as early and max-violation updates. The contributions of this dissertation sit at the intersection of machine learning and natural language processing. With regard to language processing, we consider three tasks: Coreference resolution, dependency parsing, and joint sentence segmentation and dependency parsing. For coreference resolution, we start from an existing latent tree model and extend it to accommodate non-local features drawn from a greater structural context. This requires us to sacrifice exact for approximate search, but we show that, assuming sufficiently advanced update methods are used for the structured perceptron, then the richer scope of features yields a stronger coreference model. We take a transition-based approach to dependency parsing, where dependency trees are constructed incrementally by transition system. Latent structures for transition-based parsing have previously not received enough attention, partly because the characterization of the prediction space is non-trivial. We provide a thorough analysis of this space with regard to the ArcStandard with Swap transition system. This characterization enables us to evaluate the role of latent structures in transition-based dependency parsing. Empirically we find that the utility of latent structures depend on the choice of approximate search -- for greedy search they improve performance, whereas with beam search they are on par, or sometimes slightly ahead of, previous approaches. We then go on to extend this transition system to do joint sentence segmentation and dependency parsing. We develop a transition system capable of handling this task and evaluate it on noisy, non-edited texts. With a set of carefully selected baselines and data sets we employ this system to measure the effectiveness of syntactic information for sentence segmentation. We show that, in the absence of obvious orthographic clues such as punctuation and capitalization, syntactic information can be used to improve sentence segmentation. With regard to machine learning, our contributions of course include the framework itself. The task-specific evaluations, however, allow us to probe the learning machinery along certain boundary points and draw more general conclusions. A recurring observation is that some of the standard update methods for the structured perceptron with approximate search -- e.g., early and max-violation updates -- are inadequate when the predicted structure reaches a certain size. We show that the primary problem with these updates is that they may discard training data and that this effect increases as the structure size increases. This problem can be handled by using more advanced update methods that commit to using all the available training data. Here, we propose a new update method, DLaSO, which consistently outperforms all other update methods we compare to. Moreover, while this problem potentially could be handled by an increased beam size, we also show that this cannot fully compensate for the structure size and that the more advanced methods indeed are required.Bei der automatisierten Analyse natürlicher Sprache werden in der Regel maschinelle Lernverfahren eingesetzt, um verschiedenste linguistische Information wie beispielsweise syntaktische Strukturen vorherzusagen. Structured Prediction (dt. etwa Strukturvorhersage), also der Zweig des maschinellen Lernens, der sich mit der Vorhersage komplexer Strukturen wie formalen Bäumen oder Graphen beschäftigt, hat deshalb erhebliche Beachtung in der Forschung zur automatischen Sprachverarbeitung gefunden. In manchen Fällen ist es vorteilhaft, die gesuchte linguistische Struktur nicht direkt zu modellieren und stattdessen interne Repräsentationen zu lernen, aus denen dann die gewünschte linguistische Information abgeleitet werden kann. Da die internen Repräsentationen allerdings selten direkt in Trainingsdaten verfügbar sind, sondern erst aus der linguistischen Annotation inferiert werden müssen, kann es vorkommen, dass dabei mehrere äquivalente Strukturen in Frage kommen. Anstatt nun vor dem Lernen eine Struktur beliebig auszuwählen, kann man diese Entscheidung dem Lernverfahren selbst überlassen, welches dann selbständig die für das Modell am besten passende auszuwählen lernt. Unter diesen Umständen bezeichnet man die interne, nicht a priori bekannte Repräsentation für eine gesuchte Zielstruktur als latent. Diese Dissertation stellt ein Structured Prediction Framework vor, mit dem man den Vorteil latenter Repräsentationen nutzen kann und welches gleichzeitig von konkreten Anwendungsfällen abstrahiert. Diese Modularisierung ermöglicht die Wiederverwendbarkeit und den Vergleich über mehrere Aufgaben und Aufgabenklassen hinweg. Um das Framework auf ein reales Problem anzuwenden, müssen nur einige Hyperparameter definiert und einige problemspezifische Funktionen implementiert werden. Das vorgestellte Framework basiert auf dem Structured Perceptron. Der Perceptron-Algorithmus ist ein inkrementelles Lernverfahren (eng. online learning), bei dem während des Trainings einzelne Trainingsinstanzen nacheinander betrachtet werden. In jedem Schritt wird mit dem aktuellen Modell eine Vorhersage gemacht. Stimmt die Vorhersage nicht mit dem vorgegebenen Ergebnis überein, wird das Modell durch ein entsprechendes Update angepasst und mit der nächsten Trainingsinstanz fortgefahren. Der Structured Perceptron wird im vorgestellten Framework mit Beam Search kombiniert. Beam Search ist ein approximatives Suchverfahren, welches auch in sehr großen Suchräumen effizientes Suchen erlaubt. Es kann aus diesem Grund aber keine Garantie dafür bieten, dass das gefundene Ergebnis auch das optimale ist. Das Training eines Perceptrons mit Beam Search erfordert deshalb besondere Update-Methoden, z.B. Early- oder Max-Violation-Updates, um mögliche Vorhersagefehler, die auf den Suchalgorithmus zurückgehen, auszugleichen. Diese Dissertation ist an der Schnittstelle zwischen maschinellem Lernen und maschineller Sprachverarbeitung angesiedelt. Im Bereich Sprachverarbeitung beschäftigt sie sich mit drei Aufgaben: Koreferenzresolution, Dependenzparsing und Dependenzparsing mit gleichzeitiger Satzsegmentierung. Das vorgestellte Modell zur Koreferenzresolution ist eine Erweiterung eines existierenden Modells, welches Koreferenz mit Hilfe latenter Baumstrukturen repräsentiert. Dieses Modell wird um Features erweitert, mit denen nicht-lokale Abhängigkeiten innerhalb eines größeren strukturellen Kontexts modelliert werden. Die Modellierung nicht-lokaler Abhängigkeiten macht durch die kombinatorische Explosion der Features die Verwendung eines approximativen Suchverfahrens notwendig. Es zeigt sich aber, dass das so entstandene Koreferenzmodell trotz der approximativen Suche dem Modell ohne nicht-lokale Features überlegen ist, sofern hinreichend gute Update-Verfahren beim Lernen verwendet werden. Für das Dependenzparsing verwenden wir ein transitionsbasiertes Verfahren, bei dem Dependenzbäume inkrementell durch Transitionen zwischen definierten Zuständen konstruiert werden. Im ersten Schritt erarbeiten wir eine umfassende Analyse des latenten Strukturraums eines bekannten Transitionssystems, nämlich ArcStandard mit Swap. Diese Analyse erlaubt es uns, die Rolle der latenten Strukturen in einem transitionsbasierten Dependenzparser zu evaluieren. Wir zeigen dann empirisch, dass die Nützlichkeit latenter Strukturen von der Wahl des Suchverfahrens abhängt -- in Kombination mit Greedy-Search verbessern sich die Ergebnisse, in Kombination mit Beam-Search bleiben sie gleich oder verbessern sich leicht gegenüber vergleichbaren Modellen. Für die dritte Aufgabe wird der Parser noch einmal erweitert: wir entwickeln das Transitionssystem so weiter, dass es neben syntaktischer Struktur auch Satzgrenzen vorhersagt und testen das System auf verrauschten und unredigierten Textdaten. Mit Hilfe sorgfältig ausgewählter Baselinemodelle und Testdaten messen wir den Einfluss syntaktischer Information auf die Vorhersagequalität von Satzgrenzen und zeigen, dass sich in Abwesenheit orthographischer Information wie Interpunktion und Groß- und Kleinschreibung das Ergebnis durch syntaktische Information verbessert. Zu den wissenschaftlichen Beiträgen der Arbeit gehört einerseits das Framework selbst. Unsere problemspezifischen Experimente ermöglichen es uns darüber hinaus, die Lernverfahren zu untersuchen und allgemeinere Schlußfolgerungen zu ziehen. So finden wir z.B. in mehreren Experimenten, dass die etablierten Update-Methoden, also Early- oder Max-Violation-Update, nicht mehr gut funktionieren, sobald die vorhergesagte Struktur eine gewisse Größe überschreitet. Es zeigt sich, dass das Hauptproblem dieser Methoden das Auslassen von Trainingsdaten ist, und dass sie desto mehr Daten auslassen, je größer die vorhergesagte Struktur wird. Dieses Problem kann durch bessere Update-Methoden vermieden werden, bei denen stets alle Trainingsdaten verwendet werden. Wir stellen eine neue Methode vor, DLaSO, und zeigen, dass diese Methode konsequent bessere Ergebnisse liefert als alle Vergleichsmethoden. Überdies zeigen wir, dass eine erhöhte Beamgröße beim Suchen das Problem der ausgelassenen Trainingsdaten nicht kompensieren kann und daher keine Alternative zu besseren Update-Methoden darstellt