11007578
Cross Modal Disambiguation
We consider strategies for reducing ambiguity in multi-modal data, particularly in the domain of images and text. Large data sets containing images with associated text (and vice versa) are readily available, and recent work has exploited such data to learn models for linking visual elements to semantics. This requires addressing a correspondence ambiguity because it is generally not known which parts of the images connect with which language elements. In this paper we first discuss using language processing to reduce correspondence ambiguity in loosely labeled image data. We then consider a similar problem of using visual correlates to reduce ambiguity in text with associated images. Only rudimentary image understanding is needed for this task because the image only needs to help differentiate between a limited set of choices, namely the senses of a particular word.
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.
14001621
Discriminative Language Modeling with Conditional Random Fields and the Perceptron Algorithm
This paper describes discriminative language modeling for a large vocabulary speech recognition task. We contrast two parameter estimation methods: the perceptron algorithm, and a method based on conditional random fields (CRFs). The models are encoded as deterministic weighted finite state automata, and are applied by intersecting the automata with word-lattices that are the output from a baseline recognizer. The perceptron algorithm has the benefit of automatically selecting a relatively small feature set in just a couple of passes over the training data. However, using the feature set output from the perceptron algorithm (initialized with their weights), CRF training provides an additional 0.5% reduction in word error rate, for a total 1.8% absolute reduction from the baseline of 39.2%.
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.
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.
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.
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.