197,834 research outputs found
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
Making Neural QA as Simple as Possible but not Simpler
Recent development of large-scale question answering (QA) datasets triggered
a substantial amount of research into end-to-end neural architectures for QA.
Increasingly complex systems have been conceived without comparison to simpler
neural baseline systems that would justify their complexity. In this work, we
propose a simple heuristic that guides the development of neural baseline
systems for the extractive QA task. We find that there are two ingredients
necessary for building a high-performing neural QA system: first, the awareness
of question words while processing the context and second, a composition
function that goes beyond simple bag-of-words modeling, such as recurrent
neural networks. Our results show that FastQA, a system that meets these two
requirements, can achieve very competitive performance compared with existing
models. We argue that this surprising finding puts results of previous systems
and the complexity of recent QA datasets into perspective
Neural Question Answering at BioASQ 5B
This paper describes our submission to the 2017 BioASQ challenge. We
participated in Task B, Phase B which is concerned with biomedical question
answering (QA). We focus on factoid and list question, using an extractive QA
model, that is, we restrict our system to output substrings of the provided
text snippets. At the core of our system, we use FastQA, a state-of-the-art
neural QA system. We extended it with biomedical word embeddings and changed
its answer layer to be able to answer list questions in addition to factoid
questions. We pre-trained the model on a large-scale open-domain QA dataset,
SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our
approach, we achieve state-of-the-art results on factoid questions and
competitive results on list questions
An analysis of machine translation errors on the effectiveness of an Arabic-English QA system
The aim of this paper is to investigate
how much the effectiveness of a Question
Answering (QA) system was affected
by the performance of Machine
Translation (MT) based question translation.
Nearly 200 questions were selected
from TREC QA tracks and ran through a
question answering system. It was able to
answer 42.6% of the questions correctly
in a monolingual run. These questions
were then translated manually from English
into Arabic and back into English using
an MT system, and then re-applied to
the QA system. The system was able to
answer 10.2% of the translated questions.
An analysis of what sort of translation error
affected which questions was conducted,
concluding that factoid type
questions are less prone to translation error
than others
Quality Assurance Services
The Nevada System of Higher Education (NSHE) Quality Assurance (QA) Program provides a full range of affordable QA services including records management, document and data control, training, and auditing. The program was regularly audited by the United States Department of Energy (DOE) to ensure strict compliance to regulatory requirements for nuclear facilities.
The NSHE QA Program is staffed by knowledgeable professionals who specialize in assisting smaller organizations who have no previous quality assurance experience
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