SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show how the fault-tolerance and learning capability of connectionist networks can support a flat analysis for providing more robust spoken-language processing within an overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial Intelligence Research 6(1), 199

A Dependency Parsing Approach to Biomedical Text Mining

Biomedical research is currently facing a new type of challenge: an excess of information, both in terms of raw data from experiments and in the number of scientific publications describing their results. Mirroring the focus on data mining techniques to address the issues of structured data, there has recently been great interest in the development and application of text mining techniques to make more effective use of the knowledge contained in biomedical scientific publications, accessible only in the form of natural human language. This thesis describes research done in the broader scope of projects aiming to develop methods, tools and techniques for text mining tasks in general and for the biomedical domain in particular. The work described here involves more specifically the goal of extracting information from statements concerning relations of biomedical entities, such as protein-protein interactions. The approach taken is one using full parsing—syntactic analysis of the entire structure of sentences—and machine learning, aiming to develop reliable methods that can further be generalized to apply also to other domains. The five papers at the core of this thesis describe research on a number of distinct but related topics in text mining. In the first of these studies, we assessed the applicability of two popular general English parsers to biomedical text mining and, finding their performance limited, identified several specific challenges to accurate parsing of domain text. In a follow-up study focusing on parsing issues related to specialized domain terminology, we evaluated three lexical adaptation methods. We found that the accurate resolution of unknown words can considerably improve parsing performance and introduced a domain-adapted parser that reduced the error rate of theoriginal by 10% while also roughly halving parsing time. To establish the relative merits of parsers that differ in the applied formalisms and the representation given to their syntactic analyses, we have also developed evaluation methodology, considering different approaches to establishing comparable dependency-based evaluation results. We introduced a methodology for creating highly accurate conversions between different parse representations, demonstrating the feasibility of unification of idiverse syntactic schemes under a shared, application-oriented representation. In addition to allowing formalism-neutral evaluation, we argue that such unification can also increase the value of parsers for domain text mining. As a further step in this direction, we analysed the characteristics of publicly available biomedical corpora annotated for protein-protein interactions and created tools for converting them into a shared form, thus contributing also to the unification of text mining resources. The introduced unified corpora allowed us to perform a task-oriented comparative evaluation of biomedical text mining corpora. This evaluation established clear limits on the comparability of results for text mining methods evaluated on different resources, prompting further efforts toward standardization. To support this and other research, we have also designed and annotated BioInfer, the first domain corpus of its size combining annotation of syntax and biomedical entities with a detailed annotation of their relationships. The corpus represents a major design and development effort of the research group, with manual annotation that identifies over 6000 entities, 2500 relationships and 28,000 syntactic dependencies in 1100 sentences. In addition to combining these key annotations for a single set of sentences, BioInfer was also the first domain resource to introduce a representation of entity relations that is supported by ontologies and able to capture complex, structured relationships. Part I of this thesis presents a summary of this research in the broader context of a text mining system, and Part II contains reprints of the five included publications.Siirretty Doriast

Learning Fault-tolerant Speech Parsing with SCREEN

This paper describes a new approach and a system SCREEN for fault-tolerant speech parsing. SCREEEN stands for Symbolic Connectionist Robust EnterprisE for Natural language. Speech parsing describes the syntactic and semantic analysis of spontaneous spoken language. The general approach is based on incremental immediate flat analysis, learning of syntactic and semantic speech parsing, parallel integration of current hypotheses, and the consideration of various forms of speech related errors. The goal for this approach is to explore the parallel interactions between various knowledge sources for learning incremental fault-tolerant speech parsing. This approach is examined in a system SCREEN using various hybrid connectionist techniques. Hybrid connectionist techniques are examined because of their promising properties of inherent fault tolerance, learning, gradedness and parallel constraint integration. The input for SCREEN is hypotheses about recognized words of a spoken utterance potentially analyzed by a speech system, the output is hypotheses about the flat syntactic and semantic analysis of the utterance. In this paper we focus on the general approach, the overall architecture, and examples for learning flat syntactic speech parsing. Different from most other speech language architectures SCREEN emphasizes an interactive rather than an autonomous position, learning rather than encoding, flat analysis rather than in-depth analysis, and fault-tolerant processing of phonetic, syntactic and semantic knowledge.Comment: 6 pages, postscript, compressed, uuencoded to appear in Proceedings of AAAI 9

Contributions to the Construction of Extensible Semantic Editors

This dissertation addresses the need for easier construction and extension of language tools. Specifically, the construction and extension of so-called semantic editors is considered, that is, editors providing semantic services for code comprehension and manipulation. Editors like these are typically found in state-of-the-art development environments, where they have been developed by hand. The list of programming languages available today is extensive and, with the lively creation of new programming languages and the evolution of old languages, it keeps growing. Many of these languages would benefit from proper tool support. Unfortunately, the development of a semantic editor can be a time-consuming and error-prone endeavor, and too large an effort for most language communities. Given the complex nature of programming, and the huge benefits of good tool support, this lack of tools is problematic. In this dissertation, an attempt is made at narrowing the gap between generative solutions and how state-of-the-art editors are constructed today. A generative alternative for construction of textual semantic editors is explored with focus on how to specify extensible semantic editor services. Specifically, this dissertation shows how semantic services can be specified using a semantic formalism called refer- ence attribute grammars (RAGs), and how these services can be made responsive enough for editing, and be provided also when the text in an editor is erroneous. Results presented in this dissertation have been found useful, both in industry and in academia, suggesting that the explored approach may help to reduce the effort of editor construction

Automatic error recovery for LR parsers in theory and practice

This thesis argues the need for good syntax error handling schemes in language translation systems such as compilers, and for the automatic incorporation of such schemes into parser-generators. Syntax errors are studied in a theoretical framework and practical methods for handling syntax errors are presented. The theoretical framework consists of a model for syntax errors based on the concept of a minimum prefix-defined error correction,a sentence obtainable from an erroneous string by performing edit operations at prefix-defined (parser defined) errors. It is shown that for an arbitrary context-free language, it is undecidable whether a better than arbitrary choice of edit operations can be made at a prefix-defined error. For common programming languages,it is shown that minimum-distance errors and prefix-defined errors do not necessarily coincide, and that there exists an infinite number of programs that differ in a single symbol only; sets of equivalent insertions are exhibited. Two methods for syntax error recovery are, presented. The methods are language independent and suitable for automatic generation. The first method consists of two stages, local repair followed if necessary by phrase-level repair. The second method consists of a single stage in which a locally minimum-distance repair is computed. Both methods are developed for use in the practical LR parser-generator yacc, requiring no additional specifications from the user. A scheme for the automatic generation of diagnostic messages in terms of the source input is presented. Performance of the methods in practice is evaluated using a formal method based on minimum-distance and prefix-defined error correction. The methods compare favourably with existing methods for error recovery