Parsing with automatically acquired, wide-coverage, robust, probabilistic LFG approximations

Traditionally, rich, constraint-based grammatical resources have been hand-coded. Scaling such resources beyond toy fragments to unrestricted, real text is knowledge-intensive, timeconsuming and expensive. The work reported in this thesis is part of a larger project to automate as much as possible the construction of wide-coverage, deep, constraint-based grammatical resources from treebanks. The Penn-II treebank is a large collection of parse-annotated newspaper text. We have designed a Lexical-Functional Grammar (LFG) (Kaplan and Bresnan, 1982) f-structure annotation algorithm to automatically annotate this treebank with f-structure information approximating to basic predicate-argument or dependency structures (Cahill et al., 2002c, 2004a). We then use the f-structure-annotated treebank resource to automatically extract grammars and lexical resources for parsing new text into f-structures. We have designed and implemented the Treebank Tool Suite (TTS) to support the linguistic work that seeds the automatic f-structure annotation algorithm (Cahill and van Genabith, 2002) and the F-Structure Annotation Tool (FSAT) to validate and visualise the results of automatic f-structure annotation. We have designed and implemented two PCFG-based probabilistic parsing architectures for parsing unseen text into f-structures: the pipeline and the integrated model. Both architectures parse raw text into basic, but possibly incomplete, predicate-argument structures (“proto f-structures”) with long distance dependencies (LDDs) unresolved (Cahill et al., 2002c). We have designed and implemented a method for automatically resolving LDDs at f-structure level based on a finite approximation of functional uncertainty equations (Kaplan and Zaenen, 1989) automatically acquired from the f structure-annotated treebank resource (Cahill et al., 2004b). To date, the best result achieved by our own Penn-II induced grammars is a dependency f-score of 80.33% against the PARC 700, an improvement of 0.73% over the best handcrafted grammar of (Kaplan et al., 2004). The processing architecture developed in this thesis is highly flexible: using external, state-of-the-art parsing technologies (Charniak, 2000) in our pipeline model, we achieve a dependency f-score of 81.79% against the PARC 700, an improvement of 2.19% over the results reported in Kaplan et al. (2004). We have also ported our grammar induction methodology to German and the TIGER treebank resource (Cahill et al., 2003a). We have developed a method for treebank-based, wide-coverage, deep, constraintbased grammar acquisition. The resulting PCFG-based LFG approximations parse the Penn-II treebank with wider coverage (measured in terms of complete spanning parse) and parsing results comparable to or better than those achieved by the best hand-crafted grammars, with, we believe, considerably less grammar development effort. We believe that our approach successfully addresses the knowledge-acquisition bottleneck (familiar from rule-based approaches to Al and NLP) in wide-coverage, constraint-based grammar development. Our approach can provide an attractive, wide-coverage, multilingual, deep, constraint-based grammar acquisition paradigm

Topic Modeling with Structured Priors for Text-Driven Science

Many scientific disciplines are being revolutionized by the explosion of public data on the web and social media, particularly in health and social sciences. For instance, by analyzing social media messages, we can instantly measure public opinion, understand population behaviors, and monitor events such as disease outbreaks and natural disasters. Taking advantage of these data sources requires tools that can make sense of massive amounts of unstructured and unlabeled text. Topic models, statistical models that posit low-dimensional representations of data, can uncover interesting latent structure in large text datasets and are popular tools for automatically identifying prominent themes in text. For example, prominent themes of discussion in social media might include politics and health. To be useful in scientific analyses, topic models must learn interpretable patterns that accurately correspond to real-world concepts of interest. This thesis will introduce topic models that can encode additional structures such as factorizations, hierarchies, and correlations of topics, and can incorporate supervision and domain knowledge. For example, topics about elections and Congressional legislation are related to each other (as part of a broader topic of “politics”), and certain political topics have partisan associations. These types of relations between topics can be modeled by formulating the Bayesian priors over parameters as functions of underlying “components,” which can be constrained in various ways to induce different structures. This approach is first introduced through a topic model called factorial LDA, which models a factorized structure in which topics are conceptually arranged in multiple dimensions. Factorial LDA can be used to model multiple types of information, for example topic and political ideology. We then introduce a family of structured-prior topic models called SPRITE, which creates a unifying representation that generalizes factorial LDA as well as other existing topic models, and creates a powerful framework for building new models. This thesis will also show how these topic models can be used in various scientific applications, such as extracting medical information from forums, measuring healthcare quality from patient reviews, and monitoring public opinion in social media

Thistle and Interarbora

We present a system tbr manipulating a wide class of linguistic diagrams, which is configurable and extensible, and allows deployment as a web-delivered system. A major theme of this work is the transfer of the devices of lbrmal grmnmar into the analysis and construction of diagrams