CiteFinder: a System to Find and Rank Medical Citations

This thesis presents CiteFinder, a system to find relevant citations for clinicians\u27 written content. Inclusion of citations for clinical information content makes the content more reliable through the provision of scientific articles as references, and enables clinicians to easily update their written content using new information. The proposed approach splits the content into sentences, identifies the sentences that need to be supported with citations by applying classification algorithms, and uses information retrieval and ranking techniques to extract and rank relevant citations from MEDLINE for any given sentence. Additionally, this system extracts snippets from the retrieved articles. We assessed our approach on 3,699 MEDLINE papers on the subject of Heart Failure . We implemented multi-level and weight ranking algorithms to rank the citations. This study shows that using Journal priority and Study Design type significantly improves results obtained with the traditional approach of only using the text of articles, by approximately 63%. We also show that using the full-text, rather than just the abstract text, leads to extraction of higher quality snippets

Improving Search Effectiveness through Query Log and Entity Mining

The Web is the largest repository of knowledge in the world. Everyday people contribute to make it bigger by generating new web data. Data never sleeps. Every minute someone writes a new blog post, uploads a video or comments on an article. Usually people rely on Web Search Engines for satisfying their information needs: they formulate their needs as text queries and they expect a list of highly relevant documents answering their requests. Being able to manage this massive volume of data, ensuring high quality and performance, is a challenging topic that we tackle in this thesis. In this dissertation we focus on the Web of Data: a recent approach, originated from the Semantic Web community, consisting in a collective effort to augment the existing Web with semistructured-data. We propose to manage the data explosion shifting from a retrieval model based on documents to a model enriched with entities, where an entity can describe a person, a product, a location, a company, through semi-structured information. In our work, we combine the Web of Data with an important source of knowledge: query logs, which record the interactions between the Web Search Engine and the users. Query log mining aims at extracting valuable knowledge that can be exploited to enhance users’ search experience. According to this vision, this dissertation aims at improving Web Search Engines toward the mutual use of query logs and entities. The contributions of this work are the following: we show how historical usage data can be exploited for improving performance during the snippet generation process. Secondly, we propose a query recommender system that, by combining entities with queries, leads to significant improvements to the quality of the suggestions. Furthermore, we develop a new technique for estimating the relatedness between two entities, i.e., their semantic similarity. Finally, we show that entities may be useful for automatically building explanatory statements that aim at helping the user to better understand if, and why, the suggested item can be of her interest

Efficient compression of large repetitive strings

When is comes to managing large volumes of data, general-purpose compressors such as gzip are ubiquitous. They are fast, practical and available on every modern platform from standard desktops to mobile devices. These tools exploit local redundancy in a text using a fixed-size sliding window. This window is usually very small relative to the text, however, in principle it can be as large as available memory. The window acts as a dictionary. Compression is achieved by replacing substrings with pointers to previous occurrences found in the dictionary. This type of algorithm becomes problematic when dealing with collections that are larger than physical memory, as it fails to capture any non-local redundancy, that is, repetition that occurs outside of its search window. With rapid growth in the already enormous amount of data we store and process there is a pressing need for improving compression effectiveness, reducing both storage requirements and decompression costs. However, many systems still use general-purpose compression tools on large highly repetitive data collections. In this thesis we focus on addressing this issue. We explore compression in a variety of domains where large volumes of data need to be stored and accessed, and general-purpose compression tools are cannon. First we discuss our work on web corpus compression, then we discuss the implementation of a practical index for repetitive texts that gives strong theoretical bounds in terms of size and access, and finally, we discuss our work on compression of high-throughput sequencing reads. We show that in all cases, our new methods improve on current techniques in both run-time and compression effectiveness, and provide important functionality such as fast decoding and random access

Effective summarisation for search engines

Users of information retrieval (IR) systems issue queries to find information in large collections of documents. Nearly all IR systems return answers in the form of a list of results, where each entry typically consists of the title of the underlying document, a link, and a short query-biased summary of a document's content called a snippet. As retrieval systems typically return a mixture of relevant and non-relevant answers, the role of the snippet is to guide users to identify those documents that are likely to be good answers and to ignore those that are less useful. This thesis focuses on techniques to improve the generation and evaluation of query-biased summaries for informational requests, where users typically need to inspect several documents to fulfil their information needs. We investigate the following issues: how users construct query-biased summaries, and how this compares with current automatic summarisation methods; how query expansion can be applied to sentence-level ranking to improve the quality of query-biased summaries; and, how to evaluate these summarisation approaches using sentence-level relevance data. First, through an eye tracking study, we investigate the way in which users select information from documents when they are asked to construct a query-biased summary in response to a given search request. Our analysis indicates that user behaviour differs from the assumptions of current state-of-the-art query-biased summarisation approaches. A major cause of difference resulted from vocabulary mismatch, a common IR problem. This thesis then examines query expansion techniques to improve the selection of candidate relevant sentences, and to reduce the vocabulary mismatch observed in the previous study. We employ a Cranfield-based methodology to quantitatively assess sentence ranking methods based on sentence-level relevance assessments available in the TREC Novelty track, in line with previous work. We study two aspects of sentence-level evaluation of this track. First, whether sentences that have been judged based on relevance, as in the TREC Novelty track, can also be considered to be indicative; that is, useful in terms of being part of a query-biased summary and guiding users to make correct document selections. By conducting a crowdsourcing experiment, we find that relevance and indicativeness agree around 73% of the time. Second, during our evaluations we discovered a bias that longer sentences were more likely to be judged as relevant. We then propose a novel evaluation of sentence ranking methods, which aims to isolate the sentence length bias. Using our enhanced evaluation method, we find that query expansion can effectively assist in the selection of short sentences. We conclude our investigation with a second study to examine the effectiveness of query expansion in query-biased summarisation methods to end users. Our results indicate that participants significantly tend to prefer query-biased summaries aided through expansion techniques approximately 60% of the time, for query-biased summaries comprised of short and middle length sentences. We suggest that our findings can inform the generation and display of query-biased summaries of IR systems such as search engines