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

New Retrieval Approaches Using SMART : TREC 4

The Smart information retrieval project emphasizes completely automatic approaches to the understanding and retrieval of large quantities of text. We continue our work in TREC 4, performing runs in the routing, ad-hoc, confused text, interactive, and foreign language environments. Introduction For over 30 years, the Smart project at Cornell University has been interested in the analysis, search, and retrieval of heterogeneous text databases, where the vocabulary is allowed to vary widely, and the subject matter is unrestricted. Such databases may include newspaper articles, newswire dispatches, textbooks, dictionaries, encyclopedias, manuals, magazine articles, and so on. The usual text analysis and text indexing approaches that are based on the use of thesauruses and other vocabulary control devices are difficult to apply in unrestricted text environments, because the word meanings are not stable in such circumstances and the interpretation varies depending on context. The applicabil..

Automatic Query Expansion Using SMART : TREC 3

The Smart information retrieval project emphasizes completely automatic approaches to the understanding and retrieval of large quantities of text. We continue our work in TREC 3, performing runs in the routing, ad-hoc, and foreign language environments. Our major focus is massive query expansion: adding from 300 to 530 terms to each query. These terms come from known relevant documents in the case of routing, and from just the top retrieved documents in the case of ad-hoc and Spanish. This approach improves effectiveness from 7% to 25% in the various experiments. Other ad-hoc work extends our investigations into combining global similarities, giving an overall indication of how a document matches a query, with local similarities identifying a smaller part of the document which matches the query. Using an overlapping text window definition of "local", we achieve a 16% improvement. Introduction For over 30 years, the Smart project at Cornell University has been interested in the analy..

SMART High Precision : TREC 7

The Smart information retrieval project emphasizes completely automatic approaches to the understanding and retrieval of large quantities of text. We continue our work in TREC 7, concentrating on high precision retrieval. In particular, we present an in-depth analysis of our High-Precision Track results, including offering evaluation approaches and measures for time dependent evaluation. We participated in the Query Track, making initial efforts at analyzing query variability, one of the major obstacles for improving retrieval effectiveness. Basic Indexing and Retrieval In the Smart system, the vector-processing model of retrieval is used to transform both the available information requests as well as the stored documents into vectors of the form: D i = (w i1 ; w i2 ; : : : ; w it ) where D i represents a document (or query) text and w ik is the weight of term T k in document D i . A weight of zero is used for terms that are absent from a particular document, and positive weights cha..

Abstract SMART High Precision: TREC 7

The Smart information retrieval project emphasizes completely automatic approaches to the understanding and retrieval of large quantities of text. We continue our work in TREC 7, concentrating on high precision retrieval. In particular, we present an in-depth analysis of our High-Precision Track results, including o ering evaluation approaches and measures for time dependent evaluation. We participated in the Query Track, making initial e orts at analyzing query variability, one of the major obstacles for improving retrieval e ectiveness. Basic Indexing and Retrieval In the Smart system, the vector-processing model of retrieval is used to transform both the available information requests as well as the stored documents into vectors of the form: D i =(w i1;w i2;:::;w it) where D i represents a document (or query) text and w ik is the weight of term T k in document D i. A weight of zero is used for terms that are absent from a particular document, and positive weights characterize terms actually assigned. The assumption is that t terms in all are available for the representation of the information. The basic \tf*idf " weighting schemes used within SMARThave been discussed many times. For TREC 7 we use the same basic weights and document length normalization as were developed at Cornell by Amit Singhal for TREC 4([3, 5]. Tests on various collections show that this indexing is reasonably collection independen