2965282
Exploiting auxiliary distributions in stochastic unification-based grammars
This paper describes a method for estimating conditional probability distributions over the parses of "unification-based" grammars which can utilize auxiliary distributions that are estimated by other means. We show how this can be used to incorporate information about lexical selectional preferences gathered from other sources into Stochastic "Unification-based" Grammars (SUBGs). While we apply this estimator to a Stochastic Lexical-Functional Grammar, the method is general, and should be applicable to stochastic versions of HPSGs, categorial grammars and transformational grammars.
6052790
Parsing the Wall Street Journal using a Lexical-Functional Grammar and Discriminative Estimation Techniques
We present a stochastic parsing system consisting of a Lexical-Functional Grammar (LFG), a constraint-based parser and a stochastic disambiguation model. We report on the results of applying this system to parsing the UPenn Wall Street Journal (WSJ) treebank. The model combines full and partial parsing techniques to reach full grammar coverage on unseen data. The treebank annotations are used to provide partially labeled data for discriminative statistical estimation using exponential models. Disambiguation performance is evaluated by measuring matches of predicate-argument relations on two distinct test sets. On a gold standard of manually annotated f-structures for a subset of the WSJ treebank, this evaluation reaches 79% F-score. An evaluation on a gold standard of dependency relations for Brown corpus data achieves 76% F-score.
1613345
Features and Agreement
This paper compares the consistency-based account of agreement phenomena in 'unification-based' grammars with an implication-based account based on a simple feature extension to Lambek Categorial Grammar (LCG). We show that the LCG treatment accounts for constructions that have been recognized as problematic for 'unification-based' treatments.
12779913
Recognizing disfluencies in conversational speech
We present a system for modeling disfluency in conversational speech: repairs, fillers, and self-interruption points (IPs). For each sentence, candidate repair analyses are generated by a stochastic tree adjoining grammar (TAG) noisy-channel model. A probabilistic syntactic language model scores the fluency of each analysis, and a maximum-entropy model selects the most likely analysis given the language model score and other features. Fillers are detected independently via a small set of deterministic rules, and IPs are detected by combining the output of repair and filler detection modules. In the recent Rich Transcription Fall 2004 (RT-04F) blind evaluation, systems competed to detect these three forms of disfluency under two input conditions: a best-case scenario of manually transcribed words and a fully automatic case of automatic speech recognition (ASR) output. For all three tasks and on both types of input, our system was the top performer in the evaluation
232808
Memoization of Coroutined Constraints
Some linguistic constraints cannot be effectively resolved during parsing at the location in which they are most naturally introduced. This paper shows how constraints can be propagated in a memoizing parser (such as a chart parser) in much the same way that variable bindings are, providing a general treatment of constraint coroutining in memoization. Prolog code for a simple application of our technique to Bouma and van Noord's (1994) categorial grammar analysis of Dutch is provided.
15423144
Parsing with Discontinuous Constituents
By generalizing the notion of location of a constituent to allow discontinuous loctaions, one can describe the discontinuous constituents of non-configurational languages. These discontinuous constituents can be described by a variant of definite clause grammars, and these grammars can be used in conjunction with a proof procedure to create a parser for non-configurational languages.
1340064
Sub-banded reconstructed phase spaces for speech recognition
A novel method combining filter banks and reconstructed phase spaces is proposed for the modeling and classification of speech. Reconstructed phase spaces, which are based on dynamical systems theory, have advantages over spectral-based analysis methods in that they can capture nonlinear or higher-order statistics. Recent work has shown that the natural measure of a reconstructed phase space can be used for modeling and classification of phonemes. In this work, sub-banding of speech, which has been examined for recognition of noise-corrupted speech, is studied in combination with phase space reconstruction. This sub-banding, which is motivated by empirical psychoacoustical studies, is shown to dramatically improve the phoneme classification accuracy of reconstructed phase space-based approaches. Experiments that examine the performance of fused sub-banded reconstructed phase spaces for phoneme classification are presented. Comparisons against a cepstral-based classifier show that the proposed approach is competitive with state-of-the-art methods for modeling and classification of phonemes. Combination of cepstral-based features and the sub-band RPS features shows improvement over a cepstral-only baseline.
15400971
When Are Two Protocols the Same?
A number of protocols based on the formal dialogue games of philosophy have recently been proposed for interactions between autonomous agents. Several of these proposals purport to assist agents engaged in the same types of interactions, such as persuasions and negotiations, and are superficially different. How are we to determine whether or not these proposals are substantially different? This chapter considers this question and explores several alternative definitions of equivalence of protocols.
958169
Third-Order Moments of Filtered Speech Signals for Robust Speech Recognition
Novel speech features calculated from third-order statistics of subband-filtered speech signals are introduced and studied for robust speech recognition. These features have the potential to capture nonlinear information not represented by cepstral coefficients. Also, because the features presented in this paper are based on the third-order moments, they may be more immune to Gaussian noise than cepstrals, as Gaussian distributions have zero third-order moments. Experiments on the AURORA2 database studying these features in combination with Mel-frequency cepstral coefficients (MFCC's) are presented, and some improvement over the MFCC-only baseline is shown when clean speech is used for training, though the same improvement is not seen when multi-condition training data is used.
40293864
The US Software Industry
The US software industry is a combination of survivors, successful start-ups, and companies barely getting by. What is fueling the industry's growth? First, the potential of the Internet and networked and distributed computing is immense-we have only begun to tap into the possibilities. Many of our most innovative new companies are poised to take advantage of these new technologies. Second, the business and operational components of the nation's corporations are recognizing the fundamental role that PT now plays in their success. IT organizations are not the service and support centers of the past; they are an equal partner in the overall enterprise. Finally, Americans love new technologies. The software industry provides a never-ending source of innovative toys and gadgets for home and business.
15709806
Biometrics and the threat to civil liberties
Biometrics refers to the automatic identification or verification of living persons using their enduring physical or behavioral characteristics. Biometric systems fall into two categories: authentication and identification, with authentication systems being far more common. We discuss about the Biometric system and its use in authentication and identification. We also discuss about the face recognition system and the threat caused on the civil liberties due to illegal modification of the databases in the biometric system.