Leveraging Semantic Annotations to Link Wikipedia and News Archives

The incomprehensible amount of information available online has made it difficult to retrospect on past events. We propose a novel linking problem to connect excerpts from Wikipedia summarizing events to online news articles elaborating on them. To address the linking problem, we cast it into an information retrieval task by treating a given excerpt as a user query with the goal to retrieve a ranked list of relevant news articles. We find that Wikipedia excerpts often come with additional semantics, in their textual descriptions, representing the time, geolocations, and named entities involved in the event. Our retrieval model leverages text and semantic annotations as different dimensions of an event by estimating independent query models to rank documents. In our experiments on two datasets, we compare methods that consider different combinations of dimensions and find that the approach that leverages all dimensions suits our problem best

BERT-Embedding and Citation Network Analysis based Query Expansion Technique for Scholarly Search

The enormous growth of research publications has made it challenging for academic search engines to bring the most relevant papers against the given search query. Numerous solutions have been proposed over the years to improve the effectiveness of academic search, including exploiting query expansion and citation analysis. Query expansion techniques mitigate the mismatch between the language used in a query and indexed documents. However, these techniques can suffer from introducing non-relevant information while expanding the original query. Recently, contextualized model BERT to document retrieval has been quite successful in query expansion. Motivated by such issues and inspired by the success of BERT, this paper proposes a novel approach called QeBERT. QeBERT exploits BERT-based embedding and Citation Network Analysis (CNA) in query expansion for improving scholarly search. Specifically, we use the context-aware BERT-embedding and CNA for query expansion in Pseudo-Relevance Feedback (PRF) fash-ion. Initial experimental results on the ACL dataset show that BERT-embedding can provide a valuable augmentation to query expansion and improve search relevance when combined with CNA.Comment: 1

Microblog retrieval challenges and opportunities

In recent years microblogging services have changed the way we communicate. Microblogs are a reduced version of web-blogs which are characterised by being just a few characters long. In the case of Twitter, messages known as \textit{tweets} are only 140 characters long, and are broadcasted from followees to followers organised as a social network. Microblogs such as tweets, are used to communicate up to the second information about any topic. Traffic updates, natural disaster reports, self-promotion, or product marketing are only a small portion of the type of information we can find across microblogging services. Most importantly, it has become a platform that has democratised the communication channels and empowered people into voicing their opinions. In fact, it is a very well known fact that the use Twitter amongst other social media services tilted the balance in favour of ex-president Obama when he was elected president of the USA in 2012. However, whilst the widespread use of microblogs has undoubtedly changed and shaped our current society, it is still very hard to effectively perform simple searches on such datasets due to the particular morphology of its documents. The limited character count and the ineffectiveness of state of the art retrieval models in producing relevant documents for queries, thus prompted TREC organisers to unite the research community into addressing these issues in 2011 during the first Microblog 2011 Track. This doctoral work is one of such efforts, and its focused on improving the access to microblog documents through ad-hoc searches. The first part of our work individually studies the behaviour of the state of the art retrieval models when utilised for microblog ad-hoc retrieval. First we contribute with the best configurations for each of the models studied. But more importantly, we discover how query term frequency and document length relates to the relevance of microblogs. As a result, we propose a microblog specific retrieval model, namely MBRM, which significantly outperforms the state of the art retrieval models described in this work. Furthermore we define an informativeness hypothesis in order to better understand the relevance of microblogs in terms of the presence of their inherent features or dimensions. We significantly improve the behaviour of a state of the art retrieval model by taking into consideration these dimensions as features into a linear combination re-ranking approach. Additionally we investigate the role that structure plays in determining the relevance of a microblog, by encoding the structure of relevant and non-relevant documents into two separate state machines. We then devise an approach to measure the similarity of an unobserved document towards each of these state machines, to then produce a score which is utilised for ranking. Our evaluation results demonstrate how the structure of microblogs plays a role in further differentiating relevant and non-relevant documents when ranking, by showing significantly improved results over a state of the art baseline. Subsequently we study the query performance prediction (QPP) task in terms of microblog ad-hoc retrieval. QPP represents the prediction of how well a query will be satisfied by a particular retrieval system. We study the performance of predictors in the context of microblogs and propose a number of microblog specific predictors. Finally our experimental evaluation demonstrates how our predictors outperform those in the literature in the microblog context. Finally, we address the ``vocabulary mismatch'' problem by studying the effect of utilising scores produced retrieval models as an ingredient in automatic query expansion (AQE) approaches based on pseudo relevance feedback . To this end we propose alternative approaches which do not rely directly on such scores and demonstrate higher stability when determining the most optimal terms for query expansion. In addition we propose an approach to estimate the quality of a term for query expansion. To this end we employ a classifier to determine whether a prospective query expansion term falls into a low, medium or high value category. The predictions performed by the classifier are then utilised to determine a boosting factor for such terms within an AQE approach. Then we conclude by proving that it is possible to predict the quality of terms by providing statistically enhanced results over an AQE baseline

Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval

Although more and more language pairs are covered by machine translation services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application which needs translation functionality of a relatively low level of sophistication since current models for information retrieval (IR) are still based on a bag-of-words. The Web provides a vast resource for the automatic construction of parallel corpora which can be used to train statistical translation models automatically. The resulting translation models can be embedded in several ways in a retrieval model. In this paper, we will investigate the problem of automatically mining parallel texts from the Web and different ways of integrating the translation models within the retrieval process. Our experiments on standard test collections for CLIR show that the Web-based translation models can surpass commercial MT systems in CLIR tasks. These results open the perspective of constructing a fully automatic query translation device for CLIR at a very low cost.Comment: 37 page

Terms interrelationship query expansion to improve accuracy of Quran search

Quran retrieval system is becoming an instrument for users to search for needed information. The search engine is one of the most popular search engines that successfully implemented for searching relevant verses queries. However, a major challenge to the Quran search engine is word ambiguities, specifically lexical ambiguities. With the advent of query expansion techniques for Quran retrieval systems, the performance of the Quran retrieval system has problem and issue in terms of retrieving users needed information. The results of the current semantic techniques still lack precision values without considering several semantic dictionaries. Therefore, this study proposes a stemmed terms interrelationship query expansion approach to improve Quran search results. More specifically, related terms were collected from different semantic dictionaries and then utilize to get roots of words using a stemming algorithm. To assess the performance of the stemmed terms interrelationship query expansion, experiments were conducted using eight Quran datasets from the Tanzil website. Overall, the results indicate that the stemmed terms interrelationship query expansion is superior to unstemmed terms interrelationship query expansion in Mean Average Precision with Yusuf Ali 68%, Sarawar 67%, Arberry 72%, Malay 65%, Hausa 62%, Urdu 62%, Modern Arabic 60% and Classical Arabic 59%

Design and Evaluation of Temporal Summarization Systems

Temporal Summarization (TS) is a new track introduced as part of the Text REtrieval Conference (TREC) in 2013. This track aims to develop systems which can return important updates related to an event over time. In TREC 2013, the TS track specifically used disaster related events such as earthquake, hurricane, bombing, etc. This thesis mainly focuses on building an effective TS system by using a combination of Information Retrieval techniques. The developed TS system returns updates related to disaster related events in a timely manner. By participating in TREC 2013 and with experiments conducted after TREC, we examine the effectiveness of techniques such as distributional similarity for term expansion, which can be employed in building TS systems. Also, this thesis describes the effectiveness of other techniques such as stemming, adaptive sentence selection over time and de-duplication in our system, by comparing it with other baseline systems. The second part of the thesis examines the current methodology used for evaluating TS systems. We propose a modified evaluation method which could reduce the manual effort of assessors, and also correlates well with the official track’s evaluation. We also propose a supervised learning based evaluation method, which correlates well with the official track’s evaluation of systems and could save the assessor’s time by as much as 80%

Semantic concept extraction from electronic medical records for enhancing information retrieval performance

With the healthcare industry increasingly using EMRs, there emerges an opportunity for knowledge discovery within the healthcare domain that was not possible with paper-based medical records. One such opportunity is to discover UMLS concepts from EMRs. However, with opportunities come challenges that need to be addressed. Medical verbiage is very different from common English verbiage and it is reasonable to assume extracting any information from medical text requires different protocols than what is currently used in common English text. This thesis proposes two new semantic matching models: Term-Based Matching and CUI-Based Matching. These two models use specialized biomedical text mining tools that extract medical concepts from EMRs. Extensive experiments to rank the extracted concepts are conducted on the University of Pittsburgh BLULab NLP Repository for the TREC 2011 Medical Records track dataset that consists of 101,711 EMRs that contain concepts in 34 predefined topics. This thesis compares the proposed semantic matching models against the traditional weighting equations and information retrieval tools used in the academic world today

Utilizing Knowledge Bases In Information Retrieval For Clinical Decision Support And Precision Medicine

Accurately answering queries that describe a clinical case and aim at finding articles in a collection of medical literature requires utilizing knowledge bases in capturing many explicit and latent aspects of such queries. Proper representation of these aspects needs knowledge-based query understanding methods that identify the most important query concepts as well as knowledge-based query reformulation methods that add new concepts to a query. In the tasks of Clinical Decision Support (CDS) and Precision Medicine (PM), the query and collection documents may have a complex structure with different components, such as disease and genetic variants that should be transformed to enable an effective information retrieval. In this work, we propose methods for representing domain-specific queries based on weighted concepts of different types whether exist in the query itself or extracted from the knowledge bases and top retrieved documents. Besides, we propose an optimization framework, which allows unifying query analysis and expansion by jointly determining the importance weights for the query and expansion concepts depending on their type and source. We also propose a probabilistic model to reformulate the query given genetic information in the query and collection documents. We observe significant improvement of retrieval accuracy will be obtained for our proposed methods over state-of-the-art baselines for the tasks of clinical decision support and precision medicine