16207650
Term Disambiguation in Natural Language Query for XML
Converting a natural language query sentence into a formal database query is a major challenge. We have constructed NaLIX, a natural language interface for querying XML data. Through our experience with NaLIX, we find that failures in natural language query understanding can often be dealt with as ambiguities in term meanings. These failures are typically the result of either the user's poor knowledge of the database schema or the system's lack of linguistic coverage. With automatic term expansion techniques and appropriate interactive feedback, we are able to resolve these ambiguities. In this paper, we describe our approach and present results demonstrating its effectiveness.
1293275
Getting work done on the web: supporting transactional queries
Many searches on the web have a transactional intent. We argue that pages satisfying transactional needs can be distinguished from the more common pages that have some information and links, but cannot be used to execute a transaction. Based on this hypothesis, we provide a recipe for constructing a transaction annotator. By constructing an annotator with one corpus and then demonstrating its classification performance on another,we establish its robustness. Finally, we show experimentally that a search procedure that exploits such pre-annotation greatly outperforms traditional search for retrieving transactional pages.
15691197
Verifying OWL and ORL Ontologies in PVS
The Semantic Web vision is being realized to reach the full potential of the Web. Semantic data modeling is the foundation of the Semantic Web. The Web Ontology Language (OWL) and OWL Rules Language (ORL) provides basic machinery to the semantic mark-up for data. However, there is limited tool support for OWL and no tool support currently for ORL. In this paper, we propose to model OWL and ORL language semantics in PVS specification language so that OWL and ORL ontologies can be transformed and verified in the Prototype Verification System (PVS). PVS user-defined proof strategies are also developed to automate the proof process.
6066153
Kernel Methods for Document Filtering
This paper describes the algorithms inplemented by the KerMIT consortium for its participation in the Trec 2002 Filtering track. The consortium submitted runs for the routing task using a linear SVM, for the batch task using the same SVM in combination with an innovation threshold-selection mechanism, and for the adaptive task using both a second-order perceptron and a combination of SVM and perceptron with uneven margin. Results seem to indicate that these algorithm performed relatively well on the extensive TREC benchmark.
9099317
String extraction from color airline coupon image using statistical approach
A novel technique is presented in this paper to extract strings in color images of both business settlement plan (BSP) and non-BSP airline coupon. The essential concept is to remove non-text pixels from complex coupon images, rather than extract strings directly. First we transfer color images from RGB to HSV space, which is approximate uniformed, and then remove the black component of images using the property of HSV space. A statistical approach called principal components analysis (PCA) is applied to extract strings by removing the background decorative pattern based on priori environment. Finally, a method to validate and improve performance is present.
11166931
Automatic Extraction of Hierarchical Relations from Text
Automatic extraction of semantic relationships between entity instances in an ontology is useful for attaching richer semantic metadata to documents. In this paper we propose an SVM based approach to hierarchical relation extraction, using features derived automatically from a number of GATE-based open-source language processing tools. In comparison to the previous works, we use several new features including part of speech tag, entity subtype, entity class, entity role, semantic representation of sentence and WordNet synonym set. The impact of the features on the performance is investigated, as is the impact of the relation classification hierarchy. The results show there is a trade-off among these factors for relation extraction and the features containing more information such as semantic ones can improve the performance of the ontological relation extraction task.
9911218
Design of efficiently encodable moderate-length high-rate irregular LDPC codes
This paper presents a new class of irregular low-density parity-check (LDPC) codes of moderate length (10/sup 3//spl les/n/spl les/10/sup 4/) and high rate (R/spl ges/3/4). Codes in this class admit low-complexity encoding and have lower error-rate floors than other irregular LDPC code-design approaches. It is also shown that this class of LDPC codes is equivalent to a class of systematic serial turbo codes and is an extension of irregular repeat-accumulate codes. A code design algorithm based on the combination of density evolution and differential evolution optimization with a modified cost function is presented. Moderate-length, high-rate codes with no error-rate floors down to a bit-error rate of 10/sup -9/ are presented. Although our focus is on moderate-length, high-rate codes, the proposed coding scheme is applicable to irregular LDPC codes with other lengths and rates.