1,037 research outputs found
Relation Discovery from Web Data for Competency Management
This paper describes a technique for automatically discovering associations between people and expertise from an analysis of very large data sources (including web pages, blogs and emails), using a family of algorithms that perform accurate named-entity recognition, assign different weights to terms according to an analysis of document structure, and access distances between terms in a document. My contribution is to add a social networking approach called BuddyFinder which relies on associations within a large enterprise-wide "buddy list" to help delimit the search space and also to provide a form of 'social triangulation' whereby the system can discover documents from your colleagues that contain pertinent information about you. This work has been influential in the information retrieval community generally, as it is the basis of a landmark system that achieved overall first place in every category in the Enterprise Search Track of TREC2006
Report III on Knowledge-based Mining of Complex Event Patterns: Complex Event Extraction from Real-Time News Streams
In this report, we present our research results during the fourth half-year-
phase of the project Corporate Smart Content under the working package
”Knowledge-based Mining of Complex Event Patterns”. We present here a novel
approach for real-time extraction of news, based on user specifications and by
using background knowledge from specific news domains. We create a powerful
filtering service which limits the news data to the concrete and essential
preferences of a user. In our approach, enrichment of real- time news with
background knowledge is a preprocessing step. We use a Complex Event Processor
to detect complex events from the enriched articles and match them to the user
specified query. Each time a news article is matched, its result is notified
to the user immediately. Our experimental evaluation shows that our approach
is feasible for detecting news in real- time with high precision and recall
A Factoid Question Answering System for Vietnamese
In this paper, we describe the development of an end-to-end factoid question
answering system for the Vietnamese language. This system combines both
statistical models and ontology-based methods in a chain of processing modules
to provide high-quality mappings from natural language text to entities. We
present the challenges in the development of such an intelligent user interface
for an isolating language like Vietnamese and show that techniques developed
for inflectional languages cannot be applied "as is". Our question answering
system can answer a wide range of general knowledge questions with promising
accuracy on a test set.Comment: In the proceedings of the HQA'18 workshop, The Web Conference
Companion, Lyon, Franc
Dublin City University at QA@CLEF 2008
We describe our participation in Multilingual Question Answering at CLEF 2008 using German and English as our source and target languages respectively. The system was built using UIMA (Unstructured Information Management Architecture) as underlying framework
Naive Bayes Classification in The Question and Answering System
Abstract—Question and answering (QA) system is a system to answer question based on collections of unstructured text or in the form of human language. In general, QA system consists of four stages, i.e. question analysis, documents selection, passage retrieval and answer extraction. In this study we added two processes i.e. classifying documents and classifying passage. We use Naïve Bayes for classification, Dynamic Passage Partitioning for finding answer and Lucene for document selection. The experiment was done using 100 questions from 3000 documents related to the disease and the results were compared with a system that does not use the classification process. From the test results, the system works best with the use of 10 of the most relevant documents, 5 passage with the highest score and 10 answer the closest distance. Mean Reciprocal Rank (MMR) value for QA system with classification is 0.41960 which is 4.9% better than MRR value for QA system without classificatio
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