Learning Chinese language structures with multiple views

Motivated by the inadequacy of single view approaches in many areas in NLP, we study multi-view Chinese language processing, including word segmentation, part-of-speech (POS) tagging, syntactic parsing and semantic role labeling (SRL), in this thesis. We consider three situations of multiple views in statistical NLP: (1) Heterogeneous computational models have been designed for a given problem; (2) Heterogeneous annotation data is available to train systems; (3) Supervised and unsupervised machine learning techniques are applicable. First, we comparatively analyze successful single view approaches for Chinese lexical, syntactic and semantic processing. Our analysis highlights the diversity between heterogenous systems built on different views, and motivates us to improve the state-of-the-art by combining or integrating heterogeneous approaches. Second, we study the annotation ensemble problem, i.e. learning from multiple data sets under different annotation standards. We propose a series of generalized stacking models to effectively utilize heterogeneous labeled data to reduce approximation errors for word segmentation and parsing. Finally, we are concerned with bridging the gap between unsupervised and supervised learning paradigms. We introduce feature induction solutions that harvest useful linguistic knowledge from large-scale unlabeled data and effectively use them as new features to enhance discriminative learning based systems. For word segmentation, we present a comparative study of word-based and character-based approaches. Inspired by the diversity of the two views, we design a novel stacked sub-word tagging model for joint word segmentation and POS tagging, which is robust to integrate different models, even models trained on heterogeneous annotations. To benefit from unsupervised word segmentation, we derive expressive string knowledge from unlabeled data which significantly enhances a strong supervised segmenter. For POS tagging, we introduce two linguistically motivated improvements: (1) combining syntax-free sequential tagging and syntax-based chart parsing results to better capture syntagmatic lexical relations and (2) integrating word clusters acquired from unlabeled data to better capture paradigmatic lexical relations. For syntactic parsing, we present a comparative analysis for generative PCFG-LA constituency parsing and discriminative graph-based dependency parsing. To benefit from the diversity of parsing in different formalisms, we implement a previously introduced stacking method and propose a novel Bagging model to combine complementary strengths of grammar-free and grammar-based models. In addition to the study on the syntactic formalism, we also propose a reranking model to explore heterogenous treebanks that are labeled under different annotation scheme. Finally, we continue our efforts on combining strengths of supervised and unsupervised learning, and evaluate the impact of word clustering on different syntactic processing tasks. Our work on SRL focus on improving the full parsing method with linguistically rich features and a chunking strategy. Furthermore, we developed a partial parsing based semantic chunking method, which has complementary strengths to the full parsing based method. Based on our work, Zhuang and Zong (2010) successfully improve the state-of-the-art by combining full and partial parsing based SRL systems.Motiviert durch die Unzulänglichkeit der Ansätze mit dem einzigen Ansicht in vielen Bereichen in NLP, untersuchen wir Chinesische Sprache Verarbeitung mit mehrfachen Ansichten, einschließlich Wortsegmentierung, Part-of-Speech (POS)-Tagging und syntaktische Parsing und die Kennzeichnung der semantische Rolle (SRL) in dieser Arbeit . Wir betrachten drei Situationen von mehreren Ansichten in der statistischen NLP: (1) Heterogene computergestützte Modelle sind für ein gegebenes Problem entwurft, (2) Heterogene Annotationsdaten sind verfügbar, um die Systeme zu trainieren, (3) überwachten und unüberwachten Methoden des maschinellen Lernens sind zur Verfügung gestellt. Erstens, wir analysieren vergleichsweise erfolgreiche Ansätze mit einzigen Ansicht für chinesische lexikalische, syntaktische und semantische Verarbeitung. Unsere Analyse zeigt die Unterschiede zwischen den heterogenen Systemen, die auf verschiedenen Ansichten gebaut werden, und motiviert uns, die state-of-the-Art durch die Kombination oder Integration heterogener Ansätze zu verbessern. Zweitens, untersuchen wir die Annotation Ensemble Problem, d.h. das Lernen aus mehreren Datensätzen unter verschiedenen Annotation Standards. Wir schlagen eine Reihe allgemeiner Stapeln Modelle, um eine effektive Nutzung heterogener Daten zu beschriften, und um Approximationsfehler für Wort Segmentierung und Analyse zu reduzieren. Schließlich sind wir besorgt mit der Überbrückung der Kluft zwischen unüberwachten und überwachten Lernens Paradigmen. Wir führen Induktion Feature-Lösungen, die nützliche Sprachkenntnisse von großflächigen unmarkierter Daten ernte, und die effektiv nutzen als neue Features, um die unterscheidenden Lernen basierten Systemen zu verbessern. Für die Wortsegmentierung, präsentieren wir eine vergleichende Studie der Wort-basierte und Charakter-basierten Ansätzen. Inspiriert von der Vielfalt der beiden Ansichten, entwerfen wir eine neuartige gestapelt Sub-Wort-Tagging-Modell für gemeinsame Wort-Segmentierung und POS-Tagging, die robust ist, um verschiedene Modelle zu integrieren, auch Modelle auf heterogenen Annotationen geschult. Um den unbeaufsichtigten Wortsegmentierung zu profitieren, leiten wir ausdrucksstarke Zeichenfolge Wissen von unmarkierten Daten. Diese Methode hat eine überwachte Methode erheblich verbessert. Für POS-Tagging, führen wir zwei linguistisch motiviert Verbesserungen: (1) die Kombination von Syntaxfreie sequentielle Tagging und Syntaxbasierten Grafik-Parsing-Ergebnisse, um syntagmatische lexikalische Beziehungen besser zu erfassen (2) die Integration von Wortclusteren von nicht markierte Daten, um die paradigmatische lexikalische Beziehungen besser zu erfassen. Für syntaktische Parsing präsentieren wir eine vergleichenbare Analyse für generative PCFG-LA Wahlkreis Parsing und diskriminierende Graphen-basierte Abhängigkeit Parsing. Um aus der Vielfalt der Parsen in unterschiedlichen Formalismen zu profitieren, setzen wir eine zuvor eingeführte Stacking-Methode und schlagen eine neuartige Schrumpfbeutel-Modell vor, um die ergänzenden Stärken der Grammatik und Grammatik-free-basierte Modelle zu kombinieren. Neben dem syntaktischen Formalismus, wir schlagen auch ein Modell, um heterogene reranking Baumbanken, die unter verschiedenen Annotationsschema beschriftet sind zu erkunden. Schließlich setzen wir unsere Bemühungen auf die Bündelung von Stärken des überwachten und unüberwachten Lernen, und bewerten wir die Auswirkungen der Wort-Clustering auf verschiedene syntaktische Verarbeitung Aufgaben. Unsere Arbeit an SRL ist konzentriert auf die Verbesserung der vollen Parsingsmethode mit linguistischen umfangreichen Funktionen und einer Chunkingstrategie. Weiterhin entwickelten wir eine semantische Chunkingmethode basiert auf dem partiellen Parsing, die die komplementäre Stärken gegen die die Methode basiert auf dem vollen Parsing hat. Basiert auf unserer Arbeit, Zhuang und Zong (2010) hat den aktuelle Stand erfolgreich verbessert durch die Kombination von voll-und partielle-Parsing basierte SRL Systeme

Learning with Joint Inference and Latent Linguistic Structure in Graphical Models

Constructing end-to-end NLP systems requires the processing of many types of linguistic information prior to solving the desired end task. A common approach to this problem is to construct a pipeline, one component for each task, with each system\u27s output becoming input for the next. This approach poses two problems. First, errors propagate, and, much like the childhood game of telephone , combining systems in this manner can lead to unintelligible outcomes. Second, each component task requires annotated training data to act as supervision for training the model. These annotations are often expensive and time-consuming to produce, may differ from each other in genre and style, and may not match the intended application. In this dissertation we present a general framework for constructing and reasoning on joint graphical model formulations of NLP problems. Individual models are composed using weighted Boolean logic constraints, and inference is performed using belief propagation. The systems we develop are composed of two parts: one a representation of syntax, the other a desired end task (semantic role labeling, named entity recognition, or relation extraction). By modeling these problems jointly, both models are trained in a single, integrated process, with uncertainty propagated between them. This mitigates the accumulation of errors typical of pipelined approaches. Additionally we propose a novel marginalization-based training method in which the error signal from end task annotations is used to guide the induction of a constrained latent syntactic representation. This allows training in the absence of syntactic training data, where the latent syntactic structure is instead optimized to best support the end task predictions. We find that across many NLP tasks this training method offers performance comparable to fully supervised training of each individual component, and in some instances improves upon it by learning latent structures which are more appropriate for the task

Syntactic Topic Models

The syntactic topic model (STM) is a Bayesian nonparametric model of language that discovers latent distributions of words (topics) that are both semantically and syntactically coherent. The STM models dependency parsed corpora where sentences are grouped into documents. It assumes that each word is drawn from a latent topic chosen by combining document-level features and the local syntactic context. Each document has a distribution over latent topics, as in topic models, which provides the semantic consistency. Each element in the dependency parse tree also has a distribution over the topics of its children, as in latent-state syntax models, which provides the syntactic consistency. These distributions are convolved so that the topic of each word is likely under both its document and syntactic context. We derive a fast posterior inference algorithm based on variational methods. We report qualitative and quantitative studies on both synthetic data and hand-parsed documents. We show that the STM is a more predictive model of language than current models based only on syntax or only on topics

Proceedings

Proceedings of the Workshop on Annotation and Exploitation of Parallel Corpora AEPC 2010. Editors: Lars Ahrenberg, Jörg Tiedemann and Martin Volk. NEALT Proceedings Series, Vol. 10 (2010), 98 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/10062/15893

Semantic role labeling for protein transport predicates

<p>Abstract</p> <p>Background</p> <p>Automatic semantic role labeling (SRL) is a natural language processing (NLP) technique that maps sentences to semantic representations. This technique has been widely studied in the recent years, but mostly with data in newswire domains. Here, we report on a SRL model for identifying the semantic roles of biomedical predicates describing protein transport in GeneRIFs – manually curated sentences focusing on gene functions. To avoid the computational cost of syntactic parsing, and because the boundaries of our protein transport roles often did not match up with syntactic phrase boundaries, we approached this problem with a word-chunking paradigm and trained support vector machine classifiers to classify words as being at the beginning, inside or outside of a protein transport role.</p> <p>Results</p> <p>We collected a set of 837 GeneRIFs describing movements of proteins between cellular components, whose predicates were annotated for the semantic roles AGENT, PATIENT, ORIGIN and DESTINATION. We trained these models with the features of previous word-chunking models, features adapted from phrase-chunking models, and features derived from an analysis of our data. Our models were able to label protein transport semantic roles with 87.6% precision and 79.0% recall when using manually annotated protein boundaries, and 87.0% precision and 74.5% recall when using automatically identified ones.</p> <p>Conclusion</p> <p>We successfully adapted the word-chunking classification paradigm to semantic role labeling, applying it to a new domain with predicates completely absent from any previous studies. By combining the traditional word and phrasal role labeling features with biomedical features like protein boundaries and MEDPOST part of speech tags, we were able to address the challenges posed by the new domain data and subsequently build robust models that achieved F-measures as high as 83.1. This system for extracting protein transport information from GeneRIFs performs well even with proteins identified automatically, and is therefore more robust than the rule-based methods previously used to extract protein transport roles.</p