Farhad Oroumchian

University of Wollongong in Dubai | UOWD · Faculty of Engineering and Information Sciences

Purpose User feedback inferred from the user's search-time behavior could improve the learning to rank (L2R) algorithms. Click models (CMs) present probabilistic frameworks for describing and predicting the user's clicks during search sessions. Most of these CMs are based on common assumptions such as Attractiveness, Examination and User Satisfacti...

Public opinion is among the critical types of information that often inform policy changes and political strategies. Extreme public opinion is of particular importance due to the potential it has in leading to radical behaviors and violent actions. The monitoring and analysis of extreme public opinion are therefore of great interest to government o...

Aiming at achieving sustainability and quality of life for citizens, future smart cities adopt a data-centric approach to decision making in which assets, people, and events are constantly monitored to inform decisions. Public opinion monitoring is of particular importance to governments and intelligence agencies, who seek to monitor extreme views...

The advent of technological evolution, particularly in artificial intelligence has increased the responsibility of academics towards students today. It is important to understand how students view artificial intelligence, its development and use to provide ethical framework to help develop an understanding of standards, codes of conduct, right and...

This paper proposes a design of a complete system to identify weak grip strength that is caused by multiple factors like ageing, diseases, or accidents. This paper presents a grip measurement system that comprises of force sensing resistor and flex sensor to evaluate the condition of the hand. The system is tested by gripping a pencil and a cylindr...

Purpose Incorporating users’ behavior patterns could help in the ranking process. Different click models (CMs) are introduced to model the sophisticated search-time behavior of users among which commonly used the triple of attractiveness, examination and satisfaction. Inspired by this fact and considering the psychological definitions of these conc...

Smart city analytics involves tracking, interpreting, and evaluating the sentiments and emotions that are shared via online social media channels. Sentiment analysis of social media posts has become increasingly prominent in recent years as a means of gaining insights into how members of the public perceive current affairs. The ongoing research in...

Sentiment Analysis is achieved by using Natural Language Processing (NLP) techniques and finds wide applications in analyzing social media content to determine people's opinions, attitudes, and emotions toward entities, individuals, issues, events, or topics. The accuracy of sentiment analysis depends on automatic Part-of-Speech (PoS) tagging which...

Epilepsy is a chronic neurological brain disorder that affects 50 million people globally. There are several challenges associated with the care of epileptic patients, including: 1) the timely and accurate diagnosis of the condition; 2) the long-term non-intrusive monitoring and detection of epileptic seizures in real time for suitable intervention...

With the constant social, economic, and political changes witnessed in the world, numerous events occur in cities, and lead to important reactions from the public, including riots, civil disorder, and violent actions. During those events, situational awareness is crucial to gain a good understanding of the events and their impact on public opinion,...

Time has strong influence on web search. The temporal intent of the searcher adds an important dimension to the relevance judgments of web queries. However, lack of understanding their temporal requirements increases the ambiguity of the queries, turning retrieval effectiveness improvements into a complex task. In this paper, we propose an approach...

Many user information needs are strongly influenced by time. Some of these intents are expressed by users in queries issued indistinctively over time. Others follow a seasonal pattern. Examples of the latter are the queries “Golden Globe Award”, “September 11th” or “Halloween”, which refer to seasonal events that occur or have occurred at a specifi...

Rank-aggregation or combining multiple ranked lists is the heart of meta-search engines in web information retrieval. In this paper, a novel rank-aggregation method is proposed, which utilizes both data fusion operators and reinforcement learning algorithms. Such integration enables us to use the compactness property of data fusion methods as well...

Purpose Learning to rank algorithms inherently faces many challenges. The most important challenges could be listed as high-dimensionality of the training data, the dynamic nature of Web information resources and lack of click-through data. High dimensionality of the training data affects effectiveness and efficiency of learning algorithms. Besides...

Users’ click-through data is a valuable source of information about the performance of Web search engines, but it is included in few datasets for learning to rank. In this paper, inspired by the click-through data model, a novel approach is proposed for extracting the implicit user feedback from evidence embedded in benchmarking datasets. This proc...

Blogs are one of the main user-generated contents on the web and are growing in number rapidly. The characteristics of blogs require the development of specialized search methods which are tuned for the blogosphere. In this paper, we focus on blog retrieval, which aims at ranking blogs with respect to their recurrent relevance to a user’s topic. Al...

Pseudo-relevance feedback is the basis of a category of automatic query modification techniques. Pseudo-relevance feedback methods assume the initial retrieved set of documents to be relevant. Then they use these documents to extract more relevant terms for the query or just re-weigh the user's original query. In this paper, we propose a straightfo...

Opinion leaders are the influential people who are able to shape the minds and thoughts of other people in their society. Finding opinion leaders is an important task in various domains ranging from marketing to politics. In this paper, a new effective algorithm for finding opinion leaders in a given domain in online social networks is introduced....

In this article we propose a supervised method for expanding tweet contents to improve the recall of tweet filtering task in online reputation management systems. Our method does not use any external resources. It consists of creating a K-NN classifier in three steps. In these steps the tweets labeled related and unrelated in the training set are e...

Recently, user generated content is growing rapidly and becoming one of the most important sources of information in the web. Blogosphere (the collection of blogs on the web) is one of the main sources of information in this category. User’s information needs in blogosphere are different from those of general web users. So, it is necessary to prese...

In this paper, we present our approach to author ranking subtask; which is a part of author-profiling task in RepLab 2014. In this subtask, systems are expected to detect influential authors and opinion makers on Twitter website. The systems’ output, for a given domain, must be a ranked list of authors according to their probability of being an inf...

This research suggests a method for query expansion on Arabic Information Retrieval using Expectation Maximization (EM). We employ the EM algorithm in the process of selecting relevant terms for expanding the query and weeding out the non-related terms. We tested our algorithm on INFILE test collection of CLLEF2009, and the experiments show that qu...

Due to the lack of users knowledge of the collections used by search engines and in general retrieval systems, users can not express their information need appropriately in queries. In other words, they do not have enough experience to formulate their needs to find related documents. The idea of user’s query expansion aims to help users to improve...

This book constitutes the refereed proceedings of the 7th Asia Information Retrieval Societies Conference AIRS 2011, held in Dubai, United Arab Emirates, in December 2011. The 31 revised full papers and 25 revised poster papers presented were carefully reviewed and selected from 132 submissions. All current aspects of information retrieval - in the...

In languages with high word inflation such as Arabic, stemming improves text retrieval performance by reducing words variants. We propose a change in the corpus-based stemming approach proposed by Xu and Croft for English and Spanish languages in order to stem Arabic words. We generate the conflation classes by clustering 3-gram representations of...

Due to the proliferation and abundance of information on the web, ranking algorithms play an important role in web search. Currently, there are some ranking algorithms based on content and connectivity such as BM25 and PageRank. Unfortunately, these algorithms have low precision and are not always satisfying for users. In this paper, we propose an...

The theory of Human Plausible Reasoning (HPR) is an attempt by Collins and Michalski to explain how people answer questions when they are uncertain. The theory consists of a set of patterns and a set of inferences which could be applied on those patterns. This paper, investigates the application of HPR theory to the domain of cross language filteri...

The Persian language is one of the dominant languages in the Middle East, so there are significant amount of Persian documents available on the Web. Due to the different nature of the Persian language compared to the other languages such as English, the design of information retrieval systems in Persian requires special considerations. However, the...

There are three factors involved in text classification. These are classification model, similarity measure and document representation model. In this paper, we will focus on document representation and demonstrate that the choice of document representation has a profound impact on the quality of the classifier. In our experiments, we have used the...

The need for recommendation systems to ease user navigations has become evident by growth of information on the Web. There exist many approaches of learning for Web usage-based recommendation systems. In hybrid recommendation systems, other knowledge resources, like content, semantics, and hyperlink structure of the Web site, have been utilized to...

In this study we will discuss our cross language text retrieval (CLIR) experiments of Persian ad hoc track at CLEF 2008. Two teams from University of Tehran were involved in cross language text retrieval part of the track using two different CLIR approaches that are query translation and document translation. For query translation we used a method...

Metasearch engines submit the user query to several under- lying search engines and then merge their retrieved results to generate a single list that is more e®ective to the users information needs. According to the idea behind metasearch engines, it seems that merging the results retrieved from di®erent retrieval models will improve the search cov...

With the emergence of vast resources of information, it is necessary to develop methods that retrieve the most relevant information according to needs. These retrieval methods may benefit from natural language constructs to boost their results by achieving higher precision and recall rates. In this study, we have used part of speech properties of t...

This paper describes creation of a test collection for Persian Part of Speech Tagging experiments. This collection was created by modifying a manually Part of Speech (POS) tagged Persian corpus with over two million tagged words. The original collection had a tag set of 550 tags that are more than what any machine learning algorithm can handle. The...

Interoperability of heterogeneous systems on the Web will be achieved through an agreement between the underlying ontologies. Ontology matching is an operation that takes two ontologies and determines their semantic mapping. This paper presents a method of ontology matching which is based on modeling ontologies in a vector space and estimating thei...

As the number of non-English documents is increasing dramatically on the web nowadays, the study and design of information retrieval systems for these languages is very important. The Persian language is the official language of Iran, Afghanistan and Tajikistan and is also spoken in some other countries in the Middle East, so there are significant...

There are three factors involved in text classification: the classification model, the similarity measure, and the document representation. In this chapter, we will focus on document representation and demonstrate that the choice of document representation has a profound impact on the quality of the classification.We will also show that the text qu...

Part of Speech (POS) tagging is an essential part of text processing applications. A POS tagging system assigns a tag to each word of its input text specifying its grammatical properties. Several part of speech tagging systems were developed on the well known BIJANKHAN Farsi tagged corpus that contains 2500000+ tokens and each of them has different...

With the emergence of vast resources of information, it is necessary to develop methods that retrieve most relevant information according to the users needs. These retrieval methods may benefit from natural language constructs to boost their results by achieving higher precision/recall rates. In this attempt, we have used part of speech attributes...

In this study, we apply and compare some of the methods of usage pattern discovery, like simple k-means clustering algorithm, fuzzy relational subtractive clustering algorithm, fuzzy mean field annealing (MFA) clustering and Hidden Markov Model (HMM), for recommender systems. We use metrics like prediction strength, hit ratio, precision, prediction...

Expert finding has become an important retrieval task. Expert finding is about finding people rather than documents and the goal is to retrieve a ranked list of candidates/experts with expertise on a given topic. In this paper, we describe an expert- finding system that reasons about the relevance of a candidate to a given expertise area. The syste...

Semantic interoperability is highly influenced by similarities and differences which exist between ontologies. Ontology matching as a solution for finding corresponding concepts among ontologies has emerged to facilitate semantic based negotiations of applications. This paper presents a method of ontology matching which is based on vectorizing onto...

In this paper we present a method of document representation called Rich Document Representation (RDR) to build XML retrieval engines with high specificity. RDR is a form of document representation that utilizes single words, phrases, logical terms and logical statements for representing documents. The Vector Space model is used to compute index te...

Part of Speech (POS) tagging is an essential part of text processing applications. A POS tagger assigns a tag to each word of its input text specifying its grammatical properties. One of the popular POS taggers is TnT tagger which was shown to have high accuracy in English and some other languages. It is always interesting to see how a method in on...

The Persian language is one of the languages in Middle-East, so there are significant amount of Persian documents available on the Web. But there are relatively few studies on retrieval of Persian documents in the literature. In this experimental study, we assessed term and N-gram based vector space model and a query expansion method, namely, local...

Information Retrieval (IR) systems are built with different goals in mind. Some IR systems target high precision that is to have more relevant documents on the first page of their results. Other systems may target high recall that is finding as many references as possible. In this paper we present a method of document representation called RDR to b...

One of the fundamental tasks in natural language processing is part of speech (POS) tagging. Part of speech tagging is the task of annotating each word in a text with its most appropriate syntactic category. Our main interest in this research was to see how easy it is to apply methods used in a language such as English to a new and different langua...

One of the fundamental works in natural language processing is creating a feasible corpus for evaluating effectiveness of different algorithms. In this paper, the authors report creation of test corpus of automatic part of speech tagging purposes based on the Persian tagged corpus of Prof. Bijankhan. This study includes preprocessing , statistical...