Application of stochastic grammars to understanding action

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1998.Includes bibliographical references (leaves 69-72).by Yuri A. Ivanov.M.S

Do You See What I Mean? Visual Resolution of Linguistic Ambiguities

Understanding language goes hand in hand with the ability to integrate complex contextual information obtained via perception. In this work, we present a novel task for grounded language understanding: disambiguating a sentence given a visual scene which depicts one of the possible interpretations of that sentence. To this end, we introduce a new multimodal corpus containing ambiguous sentences, representing a wide range of syntactic, semantic and discourse ambiguities, coupled with videos that visualize the different interpretations for each sentence. We address this task by extending a vision model which determines if a sentence is depicted by a video. We demonstrate how such a model can be adjusted to recognize different interpretations of the same underlying sentence, allowing to disambiguate sentences in a unified fashion across the different ambiguity types.Comment: EMNLP 201

An Efficient Probabilistic Context-Free Parsing Algorithm that Computes Prefix Probabilities

We describe an extension of Earley's parser for stochastic context-free grammars that computes the following quantities given a stochastic context-free grammar and an input string: a) probabilities of successive prefixes being generated by the grammar; b) probabilities of substrings being generated by the nonterminals, including the entire string being generated by the grammar; c) most likely (Viterbi) parse of the string; d) posterior expected number of applications of each grammar production, as required for reestimating rule probabilities. (a) and (b) are computed incrementally in a single left-to-right pass over the input. Our algorithm compares favorably to standard bottom-up parsing methods for SCFGs in that it works efficiently on sparse grammars by making use of Earley's top-down control structure. It can process any context-free rule format without conversion to some normal form, and combines computations for (a) through (d) in a single algorithm. Finally, the algorithm has simple extensions for processing partially bracketed inputs, and for finding partial parses and their likelihoods on ungrammatical inputs.Comment: 45 pages. Slightly shortened version to appear in Computational Linguistics 2

An investigation of the electrolytic plasma oxidation process for corrosion protection of pure magnesium and magnesium alloy AM50.

In this study, silicate and phosphate EPO coatings were produced on pure magnesium using an AC power source. It was found that the silicate coatings possess good wear resistance, while the phosphate coatings provide better corrosion protection. A Design of Experiment (DOE) technique, the Taguchi method, was used to systematically investigate the effect of the EPO process parameters on the corrosion protection properties of a coated magnesium alloy AM50 using a DC power. The experimental design consisted of four factors (treatment time, current density, and KOH and NaAlO2 concentrations), with three levels of each factor. Potentiodynamic polarization measurements were conducted to determine the corrosion resistance of the coated samples. The optimized processing parameters are 12 minutes, 12 mA/cm2 current density, 0.9 g/l KOH, 15.0 g/l NaAlO2. The results of the percentage contribution of each factor determined by the analysis of variance (ANOVA) imply that the KOH concentration is the most significant factor affecting the corrosion resistance of the coatings, while treatment time is a major factor affecting the thickness of the coatings. (Abstract shortened by UMI.)Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .M323. Source: Masters Abstracts International, Volume: 44-03, page: 1479. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005

An investigation of grammar design in natural-language speech-recognition.

With the growing interest and demand for human-machine interaction, much work concerning speech-recognition has been carried out over the past three decades. Although a variety of approaches have been proposed to address speech-recognition issues, such as stochastic (statistical) techniques, grammar-based techniques, techniques integrated with linguistic features, and other approaches, recognition accuracy and robustness remain among the major problems that need to be addressed. At the state of the art, most commercial speech products are constructed using grammar-based speech-recognition technology. In this thesis, we investigate a number of features involved in grammar design in natural-language speech-recognition technology. We hypothesize that: with the same domain, a semantic grammar, which directly encodes some semantic constraints into the recognition grammar, achieves better accuracy, but less robustness; a syntactic grammar defines a language with a larger size, thereby it has better robustness, but less accuracy; a word-sequence grammar, which includes neither semantics nor syntax, defines the largest language, therefore, is the most robust, but has very poor recognition accuracy. In this Master\u27s thesis, we claim that proper grammar design can achieve the appropriate compromise between recognition accuracy and robustness. The thesis has been proven by experiments using the IBM Voice-Server SDK, which consists of a VoiceXML browser, IBM ViaVoice Speech Recognition and Text-To-Speech (TTS) engines, sample applications, and other tools for developing and testing VoiceXML applications. The experimental grammars are written in the Java Speech Grammar Format (JSGF), and the testing applications are written in VoiceXML. The tentative experimental results suggest that grammar design is a good area for further study. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .S555. Source: Masters Abstracts International, Volume: 43-01, page: 0244. Adviser: Richard A. Frost. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Comparison of Language Models by Stochastic Context-Free Grammar, Bigram and Quasi-Simplified-Trigram

In this paper, we investigate the language models by stochasic context-free grammar (SCFG), bigram and quasi-trigram. For calculating of statistics of bigram and quasi-trigram, we used the set of sentences generated randomly from CFG that are legal in terms of semantics. We compared them on the perplexities for their models and the sentence recognition accuracies. The sentence recognition was experimented in the "UNIX-QA" task with the vocabulary size of 521 words. From these results, the perplexities of bigram and quasi-trigram were about 1.6 times and 1.3 times larger than the perplexity of CFG that corresponds to the most restricted grammar (perplexity=10.0), and the perplexity of SCFG is only about 1/2 of CFG. We realized that quasi-trigram had the almost same ability of modeling as the most restricted CFG when the set of plausible sentences in the task was given

Towards a unified framework for sub-lexical and supra-lexical linguistic modeling

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 171-178).Conversational interfaces have received much attention as a promising natural communication channel between humans and computers. A typical conversational interface consists of three major systems: speech understanding, dialog management and spoken language generation. In such a conversational interface, speech recognition as the front-end of speech understanding remains to be one of the fundamental challenges for establishing robust and effective human/computer communications. On the one hand, the speech recognition component in a conversational interface lives in a rich system environment. Diverse sources of knowledge are available and can potentially be beneficial to its robustness and accuracy. For example, the natural language understanding component can provide linguistic knowledge in syntax and semantics that helps constrain the recognition search space. On the other hand, the speech recognition component also faces the challenge of spontaneous speech, and it is important to address the casualness of speech using the knowledge sources available. For example, sub-lexical linguistic information would be very useful in providing linguistic support for previously unseen words, and dynamic reliability modeling may help improve recognition robustness for poorly articulated speech. In this thesis, we mainly focused on the integration of knowledge sources within the speech understanding system of a conversational interface. More specifically, we studied the formalization and integration of hierarchical linguistic knowledge at both the sub-lexical level and the supra-lexical level, and proposed a unified framework for integrating hierarchical linguistic knowledge in speech recognition using layered finite-state transducers (FSTs).(cont.) Within the proposed framework, we developed context-dependent hierarchical linguistic models at both sub-lexical and supra-lexical levels. FSTs were designed and constructed to encode both structure and probability constraints provided by the hierarchical linguistic models. We also studied empirically the feasibility and effectiveness of integrating hierarchical linguistic knowledge into speech recognition using the proposed framework. We found that, at the sub-lexical level, hierarchical linguistic modeling is effective in providing generic sub-word structure and probability constraints. Since such constraints are not restricted to a fixed system vocabulary, they can help the recognizer correctly identify previously unseen words. Together with the unknown word support from natural language understanding, a conversational interface would be able to deal with unknown words better, and can possibly incorporate them into the active recognition vocabulary on-the-fly. At the supra-lexical level, experimental results showed that the shallow parsing model built within the proposed layered FST framework with top-level n-gram probabilities and phrase-level context-dependent probabilities was able to reduce recognition errors, compared to a class n-gram model of the same order. However, we also found that its application can be limited by the complexity of the composed FSTs. This suggests that, with a much more complex grammar at the supra-lexical level, a proper tradeoff between tight knowledge integration and system complexity becomes more important ...by Xiaolong Mou.Ph.D