25,513 research outputs found
Finding Structured and Unstructured Features to Improve the Search Result of Complex Question
-Recently, search engine got challenge deal with such a natural language questions.
Sometimes, these questions are complex questions. A complex question is a question that
consists several clauses, several intentions or need long answer.
In this work we proposed that finding structured features and unstructured features of
questions and using structured data and unstructured data could improve the search result
of complex questions. According to those, we will use two approaches, IR approach and
structured retrieval, QA template.
Our framework consists of three parts. Question analysis, Resource Discovery and
Analysis The Relevant Answer. In Question Analysis we used a few assumptions, and
tried to find structured and unstructured features of the questions. Structured feature
refers to Structured data and unstructured feature refers to unstructured data. In the
resource discovery we integrated structured data (relational database) and unstructured
data (webpage) to take the advantaged of two kinds of data to improve and reach the
relevant answer. We will find the best top fragments from context of the webpage In the
Relevant Answer part, we made a score matching between the result from structured data
and unstructured data, then finally used QA template to reformulate the question.
In the experiment result, it shows that using structured feature and unstructured
feature and using both structured and unstructured data, using approach IR and QA
template could improve the search result of complex questions
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
Combining information seeking services into a meta supply chain of facts
The World Wide Web has become a vital supplier of information that allows organizations to carry on such tasks as business intelligence, security monitoring, and risk assessments. Having a quick and reliable supply of correct facts from perspective is often mission critical. By following design science guidelines, we have explored ways to recombine facts from multiple sources, each with possibly different levels of responsiveness and accuracy, into one robust supply chain. Inspired by prior research on keyword-based meta-search engines (e.g., metacrawler.com), we have adapted the existing question answering algorithms for the task of analysis and triangulation of facts. We present a first prototype for a meta approach to fact seeking. Our meta engine sends a user's question to several fact seeking services that are publicly available on the Web (e.g., ask.com, brainboost.com, answerbus.com, NSIR, etc.) and analyzes the returned results jointly to identify and present to the user those that are most likely to be factually correct. The results of our evaluation on the standard test sets widely used in prior research support the evidence for the following: 1) the value-added of the meta approach: its performance surpasses the performance of each supplier, 2) the importance of using fact seeking services as suppliers to the meta engine rather than keyword driven search portals, and 3) the resilience of the meta approach: eliminating a single service does not noticeably impact the overall performance. We show that these properties make the meta-approach a more reliable supplier of facts than any of the currently available stand-alone services
NOUS: Construction and Querying of Dynamic Knowledge Graphs
The ability to construct domain specific knowledge graphs (KG) and perform
question-answering or hypothesis generation is a transformative capability.
Despite their value, automated construction of knowledge graphs remains an
expensive technical challenge that is beyond the reach for most enterprises and
academic institutions. We propose an end-to-end framework for developing custom
knowledge graph driven analytics for arbitrary application domains. The
uniqueness of our system lies A) in its combination of curated KGs along with
knowledge extracted from unstructured text, B) support for advanced trending
and explanatory questions on a dynamic KG, and C) the ability to answer queries
where the answer is embedded across multiple data sources.Comment: Codebase: https://github.com/streaming-graphs/NOU
A Machine Learning Approach For Opinion Holder Extraction In Arabic Language
Opinion mining aims at extracting useful subjective information from reliable
amounts of text. Opinion mining holder recognition is a task that has not been
considered yet in Arabic Language. This task essentially requires deep
understanding of clauses structures. Unfortunately, the lack of a robust,
publicly available, Arabic parser further complicates the research. This paper
presents a leading research for the opinion holder extraction in Arabic news
independent from any lexical parsers. We investigate constructing a
comprehensive feature set to compensate the lack of parsing structural
outcomes. The proposed feature set is tuned from English previous works coupled
with our proposed semantic field and named entities features. Our feature
analysis is based on Conditional Random Fields (CRF) and semi-supervised
pattern recognition techniques. Different research models are evaluated via
cross-validation experiments achieving 54.03 F-measure. We publicly release our
own research outcome corpus and lexicon for opinion mining community to
encourage further research
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