131 research outputs found
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Enhancing recall and precision of web search using genetic algorithm
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to rapid growth of the number of Web pages, web users encounter two main problems, namely: many of the retrieved documents are not related to the user query which is called low precision, and many of relevant documents have not been retrieved yet which is called low recall. Information Retrieval (IR) is an essential and useful technique for Web search; thus, different approaches and techniques are developed. Because of its parallel mechanism with high-dimensional space, Genetic Algorithm (GA)
has been adopted to solve many of optimization problems where IR is one of them. This thesis proposes searching model which is based on GA to retrieve HTML
documents. This model is called IR Using GA or IRUGA. It is composed of two main units. The first unit is the document indexing unit to index the HTML documents. The second unit is the GA mechanism which applies selection, crossover, and mutation operators to produce the final result, while specially designed fitness function is applied to evaluate the documents. The performance of IRUGA is investigated using the speed of convergence of the retrieval process, precision at rank N, recall at rank N, and precision at recall N. In addition, the proposed fitness function is compared experimentally with Okapi-BM25 function and Bayesian inference network model function. Moreover, IRUGA is compared with traditional IR using the same fitness function to examine the performance in terms of time required by each technique to retrieve the documents. The new techniques
developed for document representation, the GA operators and the fitness function managed to achieves an improvement over 90% for the recall and precision measures. And the relevance of the retrieved document is much higher than that retrieved by the other models. Moreover, a massive comparison of techniques applied to GA operators is performed by highlighting the strengths and weaknesses of each existing technique of GA operators. Overall, IRUGA is a promising technique in Web search domain that provides a high quality search results in terms of recall and precision
Image retrieval using automatic region tagging
The task of tagging, annotating or labelling image content automatically with semantic keywords is a challenging problem. To automatically tag images semantically based on the objects that they contain is essential for image retrieval. In addressing these problems, we explore the techniques developed to combine textual description of images with visual features, automatic region tagging and region-based ontology image retrieval. To evaluate the techniques, we use three corpora comprising: Lonely Planet travel guide articles with images, Wikipedia articles with images and Goats comic strips. In searching for similar images or textual information specified in a query, we explore the unification of textual descriptions and visual features (such as colour and texture) of the images. We compare the effectiveness of using different retrieval similarity measures for the textual component. We also analyse the effectiveness of different visual features extracted from the images. We then investigate the best weight combination of using textual and visual features. Using the queries from the Multimedia Track of INEX 2005 and 2006, we found that the best weight combination significantly improves the effectiveness of the retrieval system. Our findings suggest that image regions are better in capturing the semantics, since we can identify specific regions of interest in an image. In this context, we develop a technique to tag image regions with high-level semantics. This is done by combining several shape feature descriptors and colour, using an equal-weight linear combination. We experimentally compare this technique with more complex machine-learning algorithms, and show that the equal-weight linear combination of shape features is simpler and at least as effective as using a machine learning algorithm. We focus on the synergy between ontology and image annotations with the aim of reducing the gap between image features and high-level semantics. Ontologies ease information retrieval. They are used to mine, interpret, and organise knowledge. An ontology may be seen as a knowledge base that can be used to improve the image retrieval process, and conversely keywords obtained from automatic tagging of image regions may be useful for creating an ontology. We engineer an ontology that surrogates concepts derived from image feature descriptors. We test the usability of the constructed ontology by querying the ontology via the Visual Ontology Query Interface, which has a formally specified grammar known as the Visual Ontology Query Language. We show that synergy between ontology and image annotations is possible and this method can reduce the gap between image features and high-level semantics by providing the relationships between objects in the image. In this thesis, we conclude that suitable techniques for image retrieval include fusing text accompanying the images with visual features, automatic region tagging and using an ontology to enrich the semantic meaning of the tagged image regions
Exploiting semantics for improving clinical information retrieval
Clinical information retrieval (IR) presents several challenges including terminology mismatch and granularity mismatch. One of the main objectives in clinical IR is to fill the semantic gap among the queries and documents and going beyond keywords matching. To address these issues, in this study we attempt to use semantic information to improve the performance of clinical IR systems by representing queries in an expressive and meaningful context. In this study we propose query context modeling to improve the effectiveness of clinical IR systems. To model query contexts we propose two novel approaches to modeling medical query contexts. The first approach concerns modeling medical query contexts based on mining semantic-based AR for improving clinical text retrieval. The query context is derived from the rules that cover the query and then weighted according to their semantic relatedness to the query concepts. In our second approach we model a representative query context by developing query domain ontology. To develop query domain ontology we extract all the concepts that have semantic relationship with the query concept(s) in UMLS ontologies. Query context represents concepts extracted from query domain ontology and weighted according to their semantic relatedness to the query concept(s). The query context is then exploited in the patient records query expansion and re-ranking for improving clinical retrieval performance. We evaluate this approach on the TREC Medical Records dataset. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based IR model
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
Spoken content retrieval beyond pipeline integration of automatic speech recognition and information retrieval
The dramatic increase in the creation of multimedia content is leading to the development of large archives in which a substantial amount of the information is in spoken form. Efficient access to this information requires effective spoken content retrieval (SCR) methods. Traditionally, SCR systems have focused on a pipeline integration of two fundamental technologies: transcription using automatic speech recognition (ASR) and search supported using text-based information retrieval (IR).
Existing SCR approaches estimate the relevance of a spoken retrieval item based on the lexical overlap between a user’s query and the textual transcriptions of the items. However, the speech signal contains other potentially valuable non-lexical information that remains largely unexploited by SCR approaches. Particularly, acoustic correlates of speech prosody, that have been shown useful to identify salient words and determine topic changes, have not been exploited by existing SCR approaches.
In addition, the temporal nature of multimedia content means that accessing content is a user intensive, time consuming process. In order to minimise user effort in locating relevant content, SCR systems could suggest playback points in retrieved content indicating the locations where the system believes relevant information may be found. This typically requires adopting a segmentation mechanism for splitting documents into smaller “elements” to be ranked and from which suitable playback points could be selected. Existing segmentation approaches do not generalise well to every possible information need or provide robustness to ASR errors.
This thesis extends SCR beyond the standard ASR and IR pipeline approach by: (i) exploring the utilisation of prosodic information as complementary evidence of topical relevance to enhance current SCR approaches; (ii) determining elements of content that, when retrieved, minimise user search effort and provide increased robustness to ASR errors; and (iii) developing enhanced evaluation measures that could better capture the factors that affect user satisfaction in SCR
Using domain-specific knowledge to improve information retrieval performance
Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal
Using Word Embeddings to Retrieve Semantically Similar Questions in Community Question Answering
International audienceThis paper focuses on question retrieval which is a crucial and tricky task in Community Question Answering (cQA). Question retrieval aims at finding historical questions that are semantically equivalent to the queried ones, assuming that the answers to the similar questions should also answer the new ones. The major challenges are the lexical gap problem as well as the verboseness in natural language. Most existing methods measure the similarity between questions based on the bag-of-words (BOWs) representation capturing no semantics between words. In this paper, we rely on word embeddings and TF-IDF for a meaningful vector representation of the questions. The similarity between questions is measured using cosine similarity based on their vector-based word representations. Experiments carried out on a real world data set from Yahoo! Answers show that our method is competetive
Source code authorship attribution
To attribute authorship means to identify the true author among many candidates for samples of work of unknown or contentious authorship. Authorship attribution is a prolific research area for natural language, but much less so for source code, with eight other research groups having published empirical results concerning the accuracy of their approaches to date. Authorship attribution of source code is the focus of this thesis. We first review, reimplement, and benchmark all existing published methods to establish a consistent set of accuracy scores. This is done using four newly constructed and significant source code collections comprising samples from academic sources, freelance sources, and multiple programming languages. The collections developed are the most comprehensive to date in the field. We then propose a novel information retrieval method for source code authorship attribution. In this method, source code features from the collection samples are tokenised, converted into n-grams, and indexed for stylistic comparison to query samples using the Okapi BM25 similarity measure. Authorship of the top ranked sample is used to classify authorship of each query, and the proportion of times that this is correct determines overall accuracy. The results show that this approach is more accurate than the best approach from the previous work for three of the four collections. The accuracy of the new method is then explored in the context of author style evolving over time, by experimenting with a collection of student programming assignments that spans three semesters with established relative timestamps. We find that it takes one full semester for individual coding styles to stabilise, which is essential knowledge for ongoing authorship attribution studies and quality control in general. We conclude the research by extending both the new information retrieval method and previous methods to provide a complete set of benchmarks for advancing the field. In the final evaluation, we show that the n-gram approaches are leading the field, with accuracy scores for some collections around 90% for a one-in-ten classification problem
Temporal Dimension of Text: Quantification, Metrics and Features
The time dimension is so inherently bound to any information space that it can hardly be ignored when describing the reality, nor can be disregarded in interpreting most information. In the pressing need to search and classify a larger amount of unstructured data with better accuracy, the temporal dimension of text documents is becoming a crucial property for information retrieval and text mining tasks.
Of all the features that characterize textual information, the time dimension is still not fully regarded, despite its richness and diversity. Temporal information retrieval is still in its infancy, while time features of documents are barely taken into account in text classification.
The temporal aspects of text can be used to better interpret the relative truthiness and the context of old information, and to determine the relevance of a document with respect to information needs and categories.
In this research, we first explore the temporal dimension of text collections in a large scale study on more than 30 million documents, quantifying its extent and showing its peculiarities and patterns, such as the relation between the creation time of documents and the mentioned time.
Then we define a comprehensive and accurate representation of the temporal aspects of documents, modeling ad-hoc temporal similarities based on metric distances between time intervals.
Results of evaluation show taking into account the temporal relevance of documents yields a significant improvement in retrieval effectiveness, over both implicit and explicit time queries, and a gain in classification accuracy when temporal features are involved.
By defining a set of temporal features to comprehensively describe the temporal scope of text documents, we show their significant relation to topical categories and how these proposed features are able to categorize documents, improving the text categorization tasks in combination with ordinary terms frequencies features
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