907916
Contextual Dependencies in Unsupervised Word Segmentation
Developing better methods for segmenting continuous text into words is important for improving the processing of Asian languages, and may shed light on how humans learn to segment speech. We propose two new Bayesian word segmentation methods that assume unigram and bigram models of word dependencies respectively. The bigram model greatly outperforms the unigram model (and previous probabilistic models), demonstrating the importance of such dependencies for word segmentation. We also show that previous probabilistic models rely crucially on sub-optimal search procedures.
9027893
A Look at Parsing and Its Applications
This paper provides a brief introduction to recent work in statistical parsing and its applications. We highlight successes to date, remaining challenges, and promising future work.
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.
2234445
Attention Shifting for Parsing Speech
We present a technique that improves the efficiency of word-lattice parsing as used in speech recognition language modeling. Our technique applies a probabilistic parser iteratively where on each iteration it focuses on a different subset of the word-lattice. The parser's attention is shifted towards word-lattice subsets for which there are few or no syntactic analyses posited. This attention-shifting technique provides a six-times increase in speed (measured as the number of parser analyses evaluated) while performing equivalently when used as the first-stage of a multi-stage parsing-based language model.
3264258
Type-driven semantic interpretation and feature dependencies in R-LFG
Once one has enriched LFG's formal machinery with the linear logic mechanisms needed for semantic interpretation as proposed by Dalrymple et. al., it is natural to ask whether these make any existing components of LFG redundant. As Dalrymple and her colleagues note, LFG's f-structure completeness and coherence constraints fall out as a by-product of the linear logic machinery they propose for semantic interpretation, thus making those f-structure mechanisms redundant. Given that linear logic machinery or something like it is independently needed for semantic interpretation, it seems reasonable to explore the extent to which it is capable of handling feature structure constraints as well. 
R-LFG represents the extreme position that all linguistically required feature structure dependencies can be captured by the resource-accounting machinery of a linear or similiar logic independently needed for semantic interpretation, making LFG's unification machinery redundant. The goal is to show that LFG linguistic analyses can be expressed as clearly and perspicuously using the smaller set of mechanisms of R-LFG as they can using the much larger set of unification-based mechanisms in LFG: if this is the case then we will have shown that positing these extra f-structure mechanisms is not linguistically warranted.
5925726
A Resource Sensitive Interpretation of Lexical Functional Grammar
This paper investigates whether the fundamental linguistic insights and intuitions of Lexical Functional Grammar (LFG), which is usually presented as a “constraint-based” linguistic theory, can be reformulated in a “resource sensitive” framework using a substructural modal logic. In the approach investigated here, LFG's f-descriptions are replaced with expressions from a multi-modal propositional logic (with permutation and possibly limited contraction). In effect, the feature structure “unification” basis of LFG's f-structures is replaced with a very different resource based mechanism. It turns out that some linguistic analyses that required non-monotonic devices in LFG (such as the “constraint equations” in the Andrews (1982) analysis of Icelandic) can be straightforwardly expressed in the framework presented here. Moreover, a Curry–Howard correspondence between proofs in this logic and λ-terms provides a semantic interpretation as a by-product of the process of showing syntactic well-formedness.
1780562
Word sense disambiguation with pictures
We introduce using images for word sense disambiguation, either alone, or in conjunction with traditional text based methods. The approach is based on a recently developed method for automatically annotating images by using a statistical model for the joint probability for image regions and words. The model itself is learned from a data base of images with associated text. To use the model for word sense disambiguation, we constrain the predicted words to be possible senses for the word under consideration. When word prediction is constrained to a narrow set of choices (such as possible senses), it can be quite reliable. We report on experiments using the resulting sense probabilities as is, as well as augmenting a state of the art text based word sense disambiguation algorithm. In order to evaluate our approach, we developed a new corpus, ImCor, which consists of a substantive portion of the Corel image data set associated with disambiguated text drawn from the SemCor corpus. Our experiments using this corpus suggest that visual information can be very useful in disambiguating word senses. It also illustrates that associated non-textual information such as image data can help ground language meaning.
7477976
Attention Metaphors: How Metaphors Guide the Cognitive Psychology of Attention
The concept of attention is defined by multiple inconsistent metaphors that scientists use to identify relevant phenomena, frame hypotheses, construct experiments, and interpret data. (1) The Filter metaphor shapes debates about partial vs. complete filtering, early vs. late selection, and information filtering vs. enhancement. (2) The Spotlight metaphor raises the issue of space- vs. object-based selection, and it guides research on the size, shape, and movement of the attentional focus. (3) The Spotlight-in-the-Brain metaphor is frequently used to interpret imaging studies of attention. (4) The debate between supramodal and pre-motor theories of attention replays the dichotomy between the Spotlight and the Vision metaphors of attention. Our analysis reveals the central role of metaphor in scientific theory and research on attention, exposes hidden assumptions behind various research strategies, and shows the need for flexibility in the use of current metaphors.
17383450
Effectiveness of a machine-level, global optimizer
We present an overview of the design of a machine-code-level, global (intraprocedural) optimizer that supports several front-ends producing code for the Hewlett-Packard Precision Architecture family of machines. The basic optimization strategy is described, including information about the division of responsibilities between various components of the compiler. Optimization algorithms are described, including a discussion of the dataflow information they require. Measurements showing the collective and individual effects of various optimizer components are presented.
The performance data presented here was collected using a preliminary version of the optimizer. Development is continuing and further improvements are expected.
26304890
Speech signal enhancement through adaptive wavelet thresholding
This paper demonstrates the application of the Bionic Wavelet Transform (BWT), an adaptive wavelet transform derived from a non-linear auditory model of the cochlea, to the task of speech signal enhancement. Results, measured objectively by Signal-to-Noise ratio (SNR) and Segmental SNR (SSNR) and subjectively by Mean Opinion Score (MOS), are given for additive white Gaussian noise as well as four different types of realistic noise environments. Enhancement is accomplished through the use of thresholding on the adapted BWT coefficients, and the results are compared to a variety of speech enhancement techniques, including Ephraim Malah filtering, iterative Wiener filtering, and spectral subtraction, as well as to wavelet denoising based on a perceptually scaled wavelet packet transform decomposition. Overall results indicate that SNR and SSNR improvements for the proposed approach are comparable to those of the Ephraim Malah filter, with BWT enhancement giving the best results of all methods for the noisiest (-10db and -5db input SNR) conditions. Subjective measurements using MOS surveys across a variety of 0db SNR noise conditions indicate enhancement quality competitive with but still lower than results for Ephraim Malah filtering and iterative Wiener filtering, but higher than the perceptually scaled wavelet method.
7031742
The effect of pruning and compression on graphical representations of the output of a speech recognizer
Large vocabulary continuous speech recognition can benefit from an efficient data structure for representing a large number of acoustic hypotheses compactly. Word graphs or lattices have been chosen as such an efficient interface between acoustic recognition engines and subsequent language processing modules. This paper first investigates the effect of pruning during acoustic decoding on the quality of word lattices and shows that by combining different pruning options (at the model level and word level), we can obtain word lattices with comparable accuracy to the original lattices and a manageable size. In order to use the word lattices as the input for a post-processing language module, they should preserve the target hypotheses and their scores while being as small as possible. In this paper, we introduce a word graph compression algorithm that significantly reduces the number of words in the graphical representation without eliminating utterance hypotheses or distorting their acoustic scores. We compare this word graph compression algorithm with several other lattice size-reducing approaches and demonstrate the relative strength of the new word graph compression algorithm for decreasing the number of words in the representation. Experiments are conducted across corpora and vocabulary sizes to determine the consistency of the pruning and compression results.