15110225
Efficient Parsing for Korean and English: A Parameterized Message- Passing Approach
This article presents an efficient, implemented approach to cross-linguistic parsing based on Government Binding (GB) Theory (Chomsky 1986) and followers. One of the drawbacks to alternative GB-based parsing approaches is that they generally adopt a filter-based paradigm. These approaches typically generate all possible candidate structures of the sentence that satisfy X theory, and then subsequently apply filters in order to eliminate those structures that violate GB principles. (See, for example, Abney 1989; Correa 1991; Dorr 1993; Fong 1991.) The current approach provides an alternative to filter-based designs that avoids these difficulties by applying principles to descriptions of structures without actually building the structures themselves. Our approach is similar to that of Lin (1993) in that structure-building is deferred until the descriptions satisfy all principles; however, the current approach differs in that it provides a parameterization mechanism along the lines of Dorr (1994) that allows the system to be ported to languages other than English. We focus particularly on the problem of processing head-final languages such as Korean. We are currently incorporating the parser into a machine translation (MT) system called PRINCITRAN. l In general, parsers of existing principle-based interlingual MT systems are exceedingly inefficient, since they tend to adopt the filter-based paradigm. We combine the benefits of the message-passing paradigm with the benefits of the parameterized approach to build a more efficient, but easily extensible system, that will ultimately be used for MT. The algorithm has been implemented in C++ and successfully tested on well-known, translationally divergent sentences. We present a general framework for parsing by message passing and describe our implementation of GB principles as attribute-value constraints. We then present the parameterization framework, demonstrating the feasibility of handling cross-linguistic variation within the message-passing framework. A technique for automatic precompilation of parameter settings is described. Finally, we compare the efficiency of the
15698938
Automatic Retrieval and Clustering of Similar Words
Bootstrapping semantics from text is one of the greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of words. The similarity measure allows us to construct a thesaurus using a parsed corpus. We then present a new evaluation methodology for the automatically constructed thesaurus. The evaluation results show that the thesaurus is significantly closer to WordNet than Roget Thesaurus is.
1394805
Using Syntactic Dependency as Local Context to Resolve Word Sense Ambiguity
Most previous corpus-based algorithms disambiguate a word with a classifier trained from previous usages of the same word. Separate classifiers have to be trained for different words. We present an algorithm that uses the same knowledge sources to disambiguate different words. The algorithm does not require a sense-tagged corpus and exploits the fact that two different words are likely to have similar meanings if they occur in identical local contexts.
183890
A Probabilistic Network of Predicates
Bayesian networks are directed acyclic graphs representing independence relationships among a set of random variables. A random variable can be regarded as a set of exhaustive and mutually exclusive propositions. We argue that there are several drawbacks resulting from the propositional nature and acyclic structure of Bayesian networks. 
 
To remedy these shortcomings, we propose a probabilistic network where nodes represent unary predicates and which may contain directed cycles. The proposed representation allows us to represent domain knowledge in a single static network even though we cannot determine the instantiations of the predicates before hand. The ability to deal with cycles also enables us to handle cyclic causal tendencies and to recognize recursive plans.
6491908
A Dependency-based Method for Evaluating Broad-Coverage Parsers
With the emergence of broad-coverage parsers, quantitative evaluation of parsers becomes increasingly more important. We propose a dependency-based method for evaluating broad-coverage parsers. The method offers several advantages over previous methods that are based on phrase boundaries The error count score. We propose here is not only more intuitively meaningful than other scores, but also more relevant to semantic interpretation. We will also present an algorithm for transforming constituency trees into dependency trees so that the evaluation method is applicable to both dependency and constituency grammars. Finally, we discuss a set of operations for modifying dependency trees that can be used lo eliminate inconsequential differences among different parse trees and allow us to selectively evaluate different aspects of a parser.
9541345
Principle-Based Parsing without Overgeneration
Overgeneration is the main source of computational complexity in previous principle-based parsers. This paper presents a message passing algorithm for principle-based parsing that avoids the overgeneration problem. This algorithm has been implemented in C++ and successfully tested with example sentences from (van Riemsdijk and Williams, 1986).
5467372
Organizing and Searching the World Wide Web of Facts - Step One: The One-Million Fact Extraction Challenge
Due to the inherent difficulty of processing noisy text, the potential of the Web as a decentralized repository of human knowledge remains largely untapped during Web search. The access to billions of binary relations among named entities would enable new search paradigms and alternative methods for presenting the search results. A first concrete step towards building large searchable repositories of factual knowledge is to derive such knowledge automatically at large scale from textual documents. Generalized contextual extraction patterns allow for fast iterative progression towards extracting one million facts of a given type (e.g., Person-BornIn-Year) from 100 million Web documents of arbitrary quality. The extraction starts from as few as 10 seed facts, requires no additional input knowledge or annotated text, and emphasizes scale and coverage by avoiding the use of syntactic parsers, named entity recognizers, gazetteers, and similar text processing tools and resources.
8110502
Data Management in RFID Applications
Nowadays, RFID applications have attracted a great deal of interest due to their increasing adoptions in supply chain management, logistics and security. They have posed many new challenges to existing underlying database technologies, such as the requirements of supporting big volume data, preserving data transition path and handling new types of queries. In this paper, we propose an efficient method to manage RFID data.We explore and take advantage of the containment relationships in the relational tables in order to support special queries in the RFID applications. The experimental evaluation conducted on an existing RDBMS demonstrates the efficiency of our method.
12904695
Computer-aided kidney segmentation on abdominal CT images
In this paper, an effective model-based approach for computer-aided kidney segmentation of abdominal CT images with anatomic structure consideration is presented. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. The proposed method is a coarse to fine segmentation approach divided into two stages. First, the candidate kidney region is extracted according to the statistical geometric location of kidney within the abdomen. This approach is applicable to images of different sizes by using the relative distance of the kidney region to the spine. The second stage identifies the kidney by a series of image processing operations. The main elements of the proposed system are: 1) the location of the spine is used as the landmark for coordinate references; 2) elliptic candidate kidney region extraction with progressive positioning on the consecutive CT images; 3) novel directional model for a more reliable kidney region seed point identification; and 4) adaptive region growing controlled by the properties of image homogeneity. In addition, in order to provide different views for the physicians, we have implemented a visualization tool that will automatically show the renal contour through the method of second-order neighborhood edge detection. We considered segmentation of kidney regions from CT scans that contain pathologies in clinical practice. The results of a series of tests on 358 images from 30 patients indicate an average correlation coefficient of up to 88% between automatic and manual segmentation
12969098
An approach to evaluate policy similarity
Recent collaborative applications and enterprises very often need to efficiently integrate their access control policies. An important step in policy integration is to analyze the similarity of policies. Existing approaches to policy similarity analysis are mainly based on logical reasoning and boolean function comparison. Such approaches are computationally expensive and do not scale well for large heterogeneous distributed environments (like Grid computing systems). In this paper, we propose a policy similarity measure as a filter phase for policy similarity analysis. This measure provides a lightweight approach to pre-compile a large amount of policies and only return the most similar policies for further evaluation. In the paper we formally define the measure, by taking into account both the case of categorical attributes and numeric attributes. Detailed algorithms are presented for the similarly computation. Results of our case study demonstrates the efficiency and practical value of our approach.
16280855
Indexing Fast Moving Objects for kNN Queries Based on Nearest Landmarks
With the rapid advancements in positioning technologies such as the Global Positioning System (GPS) and wireless communications, the tracking of continuously moving objects has become more convenient. However, this development poses new challenges to database technology since maintaining up-to-date information regarding the location of moving objects incurs an enormous amount of updates. Existing indexes can no longer keep up with the high update rate while providing speedy retrieval at the same time. This study aims to improve k nearest neighbor (kNN) query performance while reducing update costs. Our approach is based on an important observation that queries usually occur around certain places or spatial landmarks of interest, called reference points. We propose the Reference-Point-based tree (RP-tree), which is a two-layer index structure that indexes moving objects according to reference points. Experimental results show that the RP-tree achieves significant improvement over the TPR-tree.