Effectiveness of query expansion in searching the Holy Quran

Modern Arabic text is written without diacritical marks (short vowels), which causes considerable ambiguity at the word level in the absence of context. Exceptional from this is the Holy Quran, which is endorsed with short vowels and other marks to preserve the pronunciation and hence, the correctness of sensing its words. Searching for a word in vowelized text requires typing and matching all its diacritical marks, which is cumbersome and preventing learners from searching and understanding the text. The other way around, is to ignore these marks and fall in the problem of ambiguity. In this paper, we provide a novel diacritic-less searching approach to retrieve from the Quran relevant verses that match a user’s query through automatic query expansion techniques. The proposed approach utilizes a relational database search engine that is scalable, portable across RDBMS platforms, and provides fast and sophisticated retrieval. The results are presented and the applied approach reveals future directions for search engines

A Method for the Construction and Application of the Term Hierarchy Relationship Residing in Relevance Feedback

In the field of information retrieval, the information of term frequency contained in relevance feedback has been widely used. However, the analysis and application of term frequency does not cover the semantic meaning of the terms, which could make the retrieval results deviate from the user’s searching goal. Consider the semantic meaning of the terms, Wille (1992) had proposed a structured view in the dealing with the term relationships of the terms in the retrieval documents. To enhance the effectiveness of information retrieval by the dealing with the mentioned information of term hierarchy relationship, this study has developed a method of query expansion to extract and apply this information contained in relevance feedback first, and then conducted some formal tests to verify the efficiency of the method in the re-ranking of the retrieved documents. The results of the formal tests show that the proposed method of query expansion is more effective than the Rocchio’s query expansion algorithm. The contribution of this study is the disclosure of the applicability of the information of term hierarchy relationship contained in relevance feedback, and the demonstration of the application of this information

Arabic Query Expansion Using WordNet and Association Rules

Query expansion is the process of adding additional relevant terms to the original queries to improve the performance of information retrieval systems. However, previous studies showed that automatic query expansion using WordNet do not lead to an improvement in the performance. One of the main challenges of query expansion is the selection of appropriate terms. In this paper, we review this problem using Arabic WordNet and Association Rules within the context of Arabic Language. The results obtained confirmed that with an appropriate selection method, we are able to exploit Arabic WordNet to improve the retrieval performance. Our empirical results on a sub-corpus from the Xinhua collection showed that our automatic selection method has achieved a significant performance improvement in terms of MAP and recall and a better precision with the first top retrieved documents

A methodology of personalized recommendation system on mobile device for digital television viewers

With the increasing of the number of digital television (TV) channels in Thailand, this becomes a problem of information overload for TV viewers. There are mass numbers of TV programs to watch but the information about these programs is poor. Therefore, this work presents a personalized recommendation system on mobile device to recommend a TV program that matches viewer’s interests and/or needs.The main mechanism of the system is content-based similarity analysis (CBSA).Initially, the viewer defines favorite programs, and then the system utilize this list as query to find their annotations on the WWW. These annotations will be used to find other programs that are similar by using CBSA. Finally, all similar programs are grouped to the same class and stored as a dataset in a personal mobile device. For the usage, if a TV program matches the interest and specified time of viewer, the system on mobile device will notify the viewer individually

Distance matters! Cumulative proximity expansions for ranking documents

In the information retrieval process, functions that rank documents according to their estimated relevance to a query typically regard query terms as being independent. However, it is often the joint presence of query terms that is of interest to the user, which is overlooked when matching independent terms. One feature that can be used to express the relatedness of co-occurring terms is their proximity in text. In past research, models that are trained on the proximity information in a collection have performed better than models that are not estimated on data. We analyzed how co-occurring query terms can be used to estimate the relevance of documents based on their distance in text, which is used to extend a unigram ranking function with a proximity model that accumulates the scores of all occurring term combinations. This proximity model is more practical than existing models, since it does not require any co-occurrence statistics, it obviates the need to tune additional parameters, and has a retrieval speed close to competing models. We show that this approach is more robust than existing models, on both Web and newswire corpora, and on average performs equal or better than existing proximity models across collections

Multi Domain Semantic Information Retrieval Based on Topic Model

Over the last decades, there have been remarkable shifts in the area of Information Retrieval (IR) as huge amount of information is increasingly accumulated on the Web. The gigantic information explosion increases the need for discovering new tools that retrieve meaningful knowledge from various complex information sources. Thus, techniques primarily used to search and extract important information from numerous database sources have been a key challenge in current IR systems. Topic modeling is one of the most recent techniquesthat discover hidden thematic structures from large data collections without human supervision. Several topic models have been proposed in various fields of study and have been utilized extensively for many applications. Latent Dirichlet Allocation (LDA) is the most well-known topic model that generates topics from large corpus of resources, such as text, images, and audio.It has been widely used in many areas in information retrieval and data mining, providing efficient way of identifying latent topics among document collections. However, LDA has a drawback that topic cohesion within a concept is attenuated when estimating infrequently occurring words. Moreover, LDAseems not to consider the meaning of words, but rather to infer hidden topics based on a statisticalapproach. However, LDA can cause either reduction in the quality of topic words or increase in loose relations between topics. In order to solve the previous problems, we propose a domain specific topic model that combines domain concepts with LDA. Two domain specific algorithms are suggested for solving the difficulties associated with LDA. The main strength of our proposed model comes from the fact that it narrows semantic concepts from broad domain knowledge to a specific one which solves the unknown domain problem. Our proposed model is extensively tested on various applications, query expansion, classification, and summarization, to demonstrate the effectiveness of the model. Experimental results show that the proposed model significantly increasesthe performance of applications

Streaming Infrastructure and Natural Language Modeling with Application to Streaming Big Data

Streaming data are produced in great velocity and diverse variety. The vision of this research is to build an end-to-end system that handles the collection, curation and analysis of streaming data. The streaming data used in this thesis contain both numeric type data and text type data. First, in the field of data collection, we design and evaluate a data delivery framework that handles the real-time nature of streaming data. In this component, we use streaming data in automotive domain since it is suitable for testing and evaluating our data delivery system. Secondly, in the field of data curation, we use a language model to analyze two online automotive forums as an example for streaming text data curation. Last but not least, we present our approach for automated query expansion on Twitter data as an example of streaming social media data analysis. This thesis provides a holistic view of the end-to-end system we have designed, built and analyzed. To study the streaming data in automotive domain, a complex and massive amount of data is being collected from on-board sensors of operational connected vehicles (CVs), infrastructure data sources such as roadway sensors and traffic signals, mobile data sources such as cell phones, social media sources such as Twitter, and news and weather data services. Unfortunately, these data create a bottleneck at data centers for processing and retrievals of collected data, and require the deployment of additional message transfer infrastructure between data producers and consumers to support diverse CV applications. The first part of this dissertation, we present a strategy for creating an efficient and low-latency distributed message delivery system for CV systems using a distributed message delivery platform. This strategy enables large-scale ingestion, curation, and transformation of unstructured data (roadway traffic-related and roadway non-traffic-related data) into labeled and customized topics for a large number of subscribers or consumers, such as CVs, mobile devices, and data centers. We evaluate the performance of this strategy by developing a prototype infrastructure using Apache Kafka, an open source message delivery system, and compared its performance with the latency requirements of CV applications. We present experimental results of the message delivery infrastructure on two different distributed computing testbeds at Clemson University. Experiments were performed to measure the latency of the message delivery system for a variety of testing scenarios. These experiments reveal that measured latencies are less than the U.S. Department of Transportation recommended latency requirements for CV applications, which provides evidence that the system is capable for managing CV related data distribution tasks. Human-generated streaming data are large in volume and noisy in content. Direct acquisition of the full scope of human-generated data is often ineffective. In our research, we try to find an alternative resource to study such data. Common Crawl is a massive multi-petabyte dataset hosted by Amazon. It contains archived HTML web page data from 2008 to date. Common Crawl has been widely used for text mining purposes. Using data extracted from Common Crawl has several advantages over a direct crawl of web data, among which is removing the likelihood of a user\u27s home IP address becoming blacklisted for accessing a given web site too frequently. However, Common Crawl is a data sample, and so questions arise about the quality of Common Crawl as a representative sample of the original data. We perform systematic tests on the similarity of topics estimated from Common Crawl compared to topics estimated from the full data of online forums. Our target is online discussions from a user forum for car enthusiasts, but our research strategy can be applied to other domains and samples to evaluate the representativeness of topic models. We show that topic proportions estimated from Common Crawl are not significantly different than those estimated on the full data. We also show that topics are similar in terms of their word compositions, and not worse than topic similarity estimated under true random sampling, which we simulate through a series of experiments. Our research will be of interest to analysts who wish to use Common Crawl to study topics of interest in user forum data, and analysts applying topic models to other data samples. Twitter data is another example of high-velocity streaming data. We use it as an example to study the query expansion application in streaming social media data analysis. Query expansion is a problem concerned with gathering more relevant documents from a given set that cover a certain topic. Here in this thesis we outline a number of tools for a query expansion system that will allow its user to gather more relevant documents (in this case, tweets from the Twitter social media system), while discriminating from irrelevant documents. These tools include a method for triggering a given query expansion using a Jaccard similarity threshold between keywords, and a query expansion method using archived news reports to create a vector space of novel keywords. As the nature of streaming data, Twitter stream contains emerging events that are constantly changing and therefore not predictable using static queries. Since keywords used in static query method often mismatch the words used in topics around emerging events. To solve this problem, our proposed approach of automated query expansion detects the emerging events in the first place. Then we combine both local analysis and global analysis methods to generate queries for capturing the emerging topics. Experiment results show that by combining the global analysis and local analysis method, our approach can capture the semantic information in the emerging events with high efficiency

Measuring the Stability of Query Term Collocations and Using it in Document Ranking

Delivering the right information to the user is fundamental in information retrieval system. Many traditional information retrieval models assume word independence and view a document as bag-of-words, however getting the right information requires a deep understanding of the content of the document and the relationships that exist between words in the text. This study focuses on developing two new document ranking techniques, which are based on a lexical cohesive relationship of collocation. Collocation relationship is a semantic relationship that exists between words that co-occur in the same lexical environment. Two types of collocation relationship have been considered; collocation in the same grammatical structure (such as a sentence), and collocation in the same semantic structure where query terms occur in different sentences but they co-occur with the same words. In the first technique, we only considered the first type of collocation to calculate the document score; where the positional frequency of query terms co-occurrence have been used to identify collocation relationship between query terms and calculating query term’s weight. In the second technique, both types of collocation have been considered; where the co-occurrence frequency distribution within a predefined window has been used to determine query terms collocations and computing query term’s weight. Evaluation of the proposed techniques show performance gain in some of the collocations over the chosen baseline runs