617 research outputs found
Hi, how can I help you?: Automating enterprise IT support help desks
Question answering is one of the primary challenges of natural language
understanding. In realizing such a system, providing complex long answers to
questions is a challenging task as opposed to factoid answering as the former
needs context disambiguation. The different methods explored in the literature
can be broadly classified into three categories namely: 1) classification
based, 2) knowledge graph based and 3) retrieval based. Individually, none of
them address the need of an enterprise wide assistance system for an IT support
and maintenance domain. In this domain the variance of answers is large ranging
from factoid to structured operating procedures; the knowledge is present
across heterogeneous data sources like application specific documentation,
ticket management systems and any single technique for a general purpose
assistance is unable to scale for such a landscape. To address this, we have
built a cognitive platform with capabilities adopted for this domain. Further,
we have built a general purpose question answering system leveraging the
platform that can be instantiated for multiple products, technologies in the
support domain. The system uses a novel hybrid answering model that
orchestrates across a deep learning classifier, a knowledge graph based context
disambiguation module and a sophisticated bag-of-words search system. This
orchestration performs context switching for a provided question and also does
a smooth hand-off of the question to a human expert if none of the automated
techniques can provide a confident answer. This system has been deployed across
675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Over the past decade, large-scale supervised learning corpora have enabled
machine learning researchers to make substantial advances. However, to this
date, there are no large-scale question-answer corpora available. In this paper
we present the 30M Factoid Question-Answer Corpus, an enormous question answer
pair corpus produced by applying a novel neural network architecture on the
knowledge base Freebase to transduce facts into natural language questions. The
produced question answer pairs are evaluated both by human evaluators and using
automatic evaluation metrics, including well-established machine translation
and sentence similarity metrics. Across all evaluation criteria the
question-generation model outperforms the competing template-based baseline.
Furthermore, when presented to human evaluators, the generated questions appear
comparable in quality to real human-generated questions.Comment: 13 pages, 1 figure, 7 table
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Proceedings of QG2010: The Third Workshop on Question Generation
These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge".
QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)
SEMONTOQA: A Semantic Understanding-Based Ontological Framework for Factoid Question Answering
This paper presents an outline of an Ontological and Se-
mantic understanding-based model (SEMONTOQA) for an
open-domain factoid Question Answering (QA) system. The
outlined model analyses unstructured English natural lan-
guage texts to a vast extent and represents the inherent con-
tents in an ontological manner. The model locates and ex-
tracts useful information from the text for various question
types and builds a semantically rich knowledge-base that
is capable of answering different categories of factoid ques-
tions. The system model converts the unstructured texts
into a minimalistic, labelled, directed graph that we call a
Syntactic Sentence Graph (SSG). An Automatic Text In-
terpreter using a set of pre-learnt Text Interpretation Sub-
graphs and patterns tries to understand the contents of the
SSG in a semantic way. The system proposes a new fea-
ture and action based Cognitive Entity-Relationship Net-
work designed to extend the text understanding process to
an in-depth level. Application of supervised learning allows
the system to gradually grow its capability to understand
the text in a more fruitful manner. The system incorpo-
rates an effective Text Inference Engine which takes the re-
sponsibility of inferring the text contents and isolating enti-
ties, their features, actions, objects, associated contexts and
other properties, required for answering questions. A similar
understanding-based question processing module interprets
the user’s need in a semantic way. An Ontological Mapping
Module, with the help of a set of pre-defined strategies de-
signed for different classes of questions, is able to perform
a mapping between a question’s ontology with the set of
ontologies stored in the background knowledge-base. Em-
pirical verification is performed to show the usability of the
proposed model. The results achieved show that, this model
can be used effectively as a semantic understanding based
alternative QA system
Question Answering over Curated and Open Web Sources
The last few years have seen an explosion of research on the topic of automated question answering (QA), spanning the communities of information retrieval, natural language processing, and artificial intelligence. This tutorial would cover the highlights of this really active period of growth for QA to give the audience a grasp over the families of algorithms that are currently being used. We partition research contributions by the underlying source from where answers are retrieved: curated knowledge graphs, unstructured text, or hybrid corpora. We choose this dimension of partitioning as it is the most discriminative when it comes to algorithm design. Other key dimensions are covered within each sub-topic: like the complexity of questions addressed, and degrees of explainability and interactivity introduced in the systems. We would conclude the tutorial with the most promising emerging trends in the expanse of QA, that would help new entrants into this field make the best decisions to take the community forward. Much has changed in the community since the last tutorial on QA in SIGIR 2016, and we believe that this timely overview will indeed benefit a large number of conference participants
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