20,764 research outputs found
Towards a Knowledge Graph based Speech Interface
Applications which use human speech as an input require a speech interface
with high recognition accuracy. The words or phrases in the recognised text are
annotated with a machine-understandable meaning and linked to knowledge graphs
for further processing by the target application. These semantic annotations of
recognised words can be represented as a subject-predicate-object triples which
collectively form a graph often referred to as a knowledge graph. This type of
knowledge representation facilitates to use speech interfaces with any spoken
input application, since the information is represented in logical, semantic
form, retrieving and storing can be followed using any web standard query
languages. In this work, we develop a methodology for linking speech input to
knowledge graphs and study the impact of recognition errors in the overall
process. We show that for a corpus with lower WER, the annotation and linking
of entities to the DBpedia knowledge graph is considerable. DBpedia Spotlight,
a tool to interlink text documents with the linked open data is used to link
the speech recognition output to the DBpedia knowledge graph. Such a
knowledge-based speech recognition interface is useful for applications such as
question answering or spoken dialog systems.Comment: Under Review in International Workshop on Grounding Language
Understanding, Satellite of Interspeech 201
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
Query-Based Summarization using Rhetorical Structure Theory
Research on Question Answering is focused mainly on classifying the question type and finding
the answer. Presenting the answer in a way that suits the userâs needs has received little
attention. This paper shows how existing question answering systemsâwhich aim at finding
precise answers to questionsâcan be improved by exploiting summarization techniques to extract
more than just the answer from the document in which the answer resides. This is done
using a graph search algorithm which searches for relevant sentences in the discourse structure,
which is represented as a graph. The Rhetorical Structure Theory (RST) is used to create a
graph representation of a text document. The output is an extensive answer, which not only
answers the question, but also gives the user an opportunity to assess the accuracy of the answer
(is this what I am looking for?), and to find additional information that is related to the question,
and which may satisfy an information need. This has been implemented in a working multimodal
question answering system where it operates with two independently developed question
answering modules
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
Improve and Implement an Open Source Question Answering System
A question answer system takes queries from the user in natural language and returns a short concise answer which best fits the response to the question. This report discusses the integration and implementation of question answer systems for English and Hindi as part of the open source search engine Yioop. We have implemented a question answer system for English and Hindi, keeping in mind users who use these languages as their primary language. The user should be able to query a set of documents and should get the answers in the same language. English and Hindi are very different when it comes to language structure, characters etc. We have implemented the Question Answer System so that it supports localization and improved Part of Speech tagging performance by storing the lexicon in the database instead of a file based lexicon. We have implemented a brill tagger variant for Part of Speech tagging of Hindi phrases and grammar rules for triplet extraction. We also improve Yioopâs lexical data handling support by allowing the user to add named entities. Our improvements to Yioop were then evaluated by comparing the retrieved answers against a dataset of answers known to be true. The test data for the question answering system included creating 2 indexes, 1 each for English and Hindi. These were created by configuring Yioop to crawl 200,000 wikipedia pages for each crawl. The crawls were configured to be domain specific so that English index consists of pages restricted to English text and Hindi index is restricted to pages with Hindi text. We then used a set of 50 questions on the English and Hindi systems. We recored, Hindi system to have an accuracy of about 55% for simple factoid questions and English question answer system to have an accuracy of 63%
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