The goal of this thesis is to explore the CSSBox experimental rendering engine, to explore the possibility of its expansion on incremental rendering of documents and then to propose the necessary modifications. The opening chapters contain an overview of existing possibilities and subsequently, the solution is proposed. The proposed changes are implemented and tested. Experiments were performed and results evaluated. The conclusion is dedicated to the evaluation of results and options for further development are outlined.

XNL-Soar is a specialized implementation of the Soar cognitive architecture. The version of XNL-Soar described in this thesis builds upon and extends prior research (Lewis, 1993; Rytting,2000) using Soar for natural language processing. This thesis describes the updates made to operators creating syntactic structure and the improved coverage of syntactic phenomena. It describes the addition of semantic structure building capability. This thesis also details the implementation of semantic memory and describes two experiments utilizing semantic memory in structural disambiguation. This thesis shows that XNL-Soar, as currently instantiated, resolves ambiguities common in language using strategies and resources including: reanalysis via snip operators, use of data-driven techniques with annotated corpora, and complex part-of-speech and word sense processing based on WordNet.

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This thesis investigates the role of linguistically-motivated generative models of syntax and semantic structure in natural language processing (NLP). Syntactic well-formedness is crucial in language generation, but most statistical models do not account for the hierarchical structure of sentences. Many applications exhibiting natural language understanding rely on structured semantic representations to enable querying, inference and reasoning. Yet most semantic parsers produce domain-specific or inadequately expressive representations. We propose a series of generative transition-based models for dependency syntax which can be applied as both parsers and language models while being amenable to supervised or unsupervised learning. Two models are based on Markov assumptions commonly made in NLP: The first is a Bayesian model with hierarchical smoothing, the second is parameterised by feed-forward neural networks. The Bayesian model enables careful analysis of the structure of the conditioning contexts required for generative parsers, but the neural network is more accurate. As a language model the syntactic neural model outperforms both the Bayesian model and n-gram neural networks, pointing to the complementary nature of distributed and structured representations for syntactic prediction. We propose approximate inference methods based on particle filtering. The third model is parameterised by recurrent neural networks (RNNs), dropping the Markov assumptions. Exact inference with dynamic programming is made tractable here by simplifying the structure of the conditioning contexts. We then shift the focus to semantics and propose models for parsing sentences to labelled semantic graphs. We introduce a transition-based parser which incrementally predicts graph nodes (predicates) and edges (arguments). This approach is contrasted against predicting top-down graph traversals. RNNs and pointer networks are key components in approaching graph parsing as an incremental prediction problem. The RNN architecture is augmented to condition the model explicitly on the transition system configuration. We develop a robust parser for Minimal Recursion Semantics, a linguistically-expressive framework for compositional semantics which has previously been parsed only with grammar-based approaches. Our parser is much faster than the grammar-based model, while the same approach improves the accuracy of neural Abstract Meaning Representation parsing.

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ここに掲載した著作物の利用に関する注意 本著作物の著作権は（社）情報処理学会に帰属します。 本著作物は著作権者である情報処理学会の許可のもとに掲載するものです。 ご利用に当たっては「著作権法」ならびに「情報処理学会倫理綱領」 に従うことをお願いいたします。 Notice for the use of this material The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). This material is published on this web site with the agreement of the author (s) and the IPSJ. Please be complied with Copyright Law of Japan and the Code of Ethics of the IPSJ if any users wish to reproduce, make derivative work, distribute or make available to the public any part or whole thereof. All Rights Reserved, Copyright (C) Information Processing Society of Japan. Comments are welcome. Mail to address: 　editj<at>ipsj.or.jp, please.

ここに掲載した著作物の利用に関する注意 本著作物の著作権は（社）情報処理学会に帰属します。 本著作物は著作権者である情報処理学会の許可のもとに掲載するものです。 ご利用に当たっては「著作権法」ならびに「情報処理学会倫理綱領」 に従うことをお願いいたします。 Notice for the use of this material The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). This material is published on this web site with the agreement of the author (s) and the IPSJ. Please be complied with Copyright Law of Japan and the Code of Ethics of the IPSJ if any users wish to reproduce, make derivative work, distribute or make available to the public any part or whole thereof. All Rights Reserved, Copyright (C) Information Processing Society of Japan. Comments are welcome. Mail to address: 　editj<at>ipsj.or.jp, please.

This dissertation explores the intersection of information retrieval and universal source coding techniques and studies an optimal multidimensional source representation from an information theoretic point of view. Previous research on information retrieval particularly focus on learning probabilistic or deterministic source models based on primarily two different types of source representations, e.g., fixed-shape partitions or uniform regions. We study the limitations of the conventional source representations on capturing the semantics of the given multidimensional source sequences and propose a new type of primitive source representation generated by a universal source coding technique. We propose a multidimensional incremental parsing algorithm extended from the Lempel-Ziv incremental parsing and its three component schemes for multidimensional source coding. The properties of the proposed coding algorithm are exploited under two-dimensional lossless and lossy source coding. By the proposed coding algorithm, a given multidimensional source sequence is parsed into a number of variable-size patches. We call this methodology a parsed representation. Based on the source representation, we propose an information retrieval framework that analyzes a set of source sequences under a linguistic processing technique and implemented content-based image retrieval systems. We examine the relevance of the proposed source representation by comparing it with the conventional representation of visual information. To further extend the proposed framework, we apply a probabilistic linguistic processing technique to modeling the latent aspects of a set of documents. In addition, beyond the symbol-wise pattern matching paradigm employed in the source coding and the image retrieval systems, we devise a robust pattern matching that compares the first- and second-order statistics of source patches. Qualitative and quantitative analysis of the proposed framework justifies the superiority of the proposed information retrieval framework based on the parsed representation. The proposed source representation technique and the information retrieval frameworks encourage future work in exploiting a systematic way of understanding multidimensional sources that parallels a linguistic structure.