610 research outputs found
Reimagining Retrieval Augmented Language Models for Answering Queries
We present a reality check on large language models and inspect the promise
of retrieval augmented language models in comparison. Such language models are
semi-parametric, where models integrate model parameters and knowledge from
external data sources to make their predictions, as opposed to the parametric
nature of vanilla large language models. We give initial experimental findings
that semi-parametric architectures can be enhanced with views, a query
analyzer/planner, and provenance to make a significantly more powerful system
for question answering in terms of accuracy and efficiency, and potentially for
other NLP task
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
Implementation of a knowledge discovery and enhancement module from structured information gained from unstructured sources of information
Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201
Rel2Graph: Automated Mapping From Relational Databases to a Unified Property Knowledge Graph
Although a few approaches are proposed to convert relational databases to
graphs, there is a genuine lack of systematic evaluation across a wider
spectrum of databases. Recognising the important issue of query mapping, this
paper proposes an approach Rel2Graph, an automatic knowledge graph construction
(KGC) approach from an arbitrary number of relational databases. Our approach
also supports the mapping of conjunctive SQL queries into pattern-based NoSQL
queries. We evaluate our proposed approach on two widely used relational
database-oriented datasets: Spider and KaggleDBQA benchmarks for semantic
parsing. We employ the execution accuracy (EA) metric to quantify the
proportion of results by executing the NoSQL queries on the property knowledge
graph we construct that aligns with the results of SQL queries performed on
relational databases. Consequently, the counterpart property knowledge graph of
benchmarks with high accuracy and integrity can be ensured. The code and data
will be publicly available. The code and data are available at
github\footnote{https://github.com/nlp-tlp/Rel2Graph}
Automatic Comprehension of Customer Queries for Feedback Generation
A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg in fulfillment of the requirements for the degree of Master of Science, 2018One major challenge in customer-driven industries is the response to large volumes ofqueries. Inresponsetothisbusinessneed,FrequentlyAskedQuestions(FAQs)have been used for over four decades to provide customers with a repository of questions and associated answers. However, FAQs require some efforts on the part of the customers to search, especially when the FAQ repository is large and poorly indexed or structured. Thisevengetsdifficultwhenanorganisationhashundredsofqueriesinits repository of FAQs. One way of dealing with this rigorous task is to allow customers to ask their questions in a Natural Language, extract the meaning of the input text and automatically provide feedback from a pool of FAQs. This is an Information Retrieval (IR) problem, in Natural Language Processing (NLP). This research work, presents the first application of Jumping Finite Automata (JFA) — an abstract computing machine — in performing this IR task. This methodology involves the abstraction of all FAQs to a JFA and applying algorithms to map customer queries to the underlying JFA of all possible queries. A data set of FAQs from a university’s Computer and Network Service (CNS) was used as test case. A prototype chat-bot application was developed that takes customer queries in a chat, automatically maps them to a FAQ, and presents the corresponding answer to the user. This research is expected to be the first of such applications of JFA in comprehending customer queries.XL201
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