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Full-Text Articles in Computer Sciences
Neural Methods For Answer Passage Retrieval Over Sparse Collections, Daniel Cohen
Neural Methods For Answer Passage Retrieval Over Sparse Collections, Daniel Cohen
Doctoral Dissertations
Recent advances in machine learning have allowed information retrieval (IR) techniques to advance beyond the stage of handcrafting domain specific features. Specifically, deep neural models incorporate varying levels of features to learn whether a document answers the information need of a query. However, these neural models rely on a large number of parameters to successfully learn a relation between a query and a relevant document.
This reliance on a large number of parameters, combined with the current methods of optimization relying on small updates necessitates numerous samples to allow the neural model to converge on an effective relevance function. This …
Neural Generative Models And Representation Learning For Information Retrieval, Qingyao Ai
Neural Generative Models And Representation Learning For Information Retrieval, Qingyao Ai
Doctoral Dissertations
Information Retrieval (IR) concerns about the structure, analysis, organization, storage, and retrieval of information. Among different retrieval models proposed in the past decades, generative retrieval models, especially those under the statistical probabilistic framework, are one of the most popular techniques that have been widely applied to Information Retrieval problems. While they are famous for their well-grounded theory and good empirical performance in text retrieval, their applications in IR are often limited by their complexity and low extendability in the modeling of high-dimensional information. Recently, advances in deep learning techniques provide new opportunities for representation learning and generative models for information …
Poetry: Identification, Entity Recognition, And Retrieval, John J. Foley Iv
Poetry: Identification, Entity Recognition, And Retrieval, John J. Foley Iv
Doctoral Dissertations
Modern advances in natural language processing (NLP) and information retrieval (IR) provide for the ability to automatically analyze, categorize, process and search textual resources. However, generalizing these approaches remains an open problem: models that appear to understand certain types of data must be re-trained on other domains. Often, models make assumptions about the length, structure, discourse model and vocabulary used by a particular corpus. Trained models can often become biased toward an original dataset, learning that – for example – all capitalized words are names of people or that short documents are more relevant than longer documents. As a result, …
Efficient Inference, Search And Evaluation For Latent Variable Models Of Text With Applications To Information Retrieval And Machine Translation, Kriste Krstovski
Efficient Inference, Search And Evaluation For Latent Variable Models Of Text With Applications To Information Retrieval And Machine Translation, Kriste Krstovski
Doctoral Dissertations
Latent variable models of text, such as topic models, have been explored in many areas of natural language processing, information retrieval and machine translation to aid tasks such as exploratory data analysis, automated topic clustering and finding similar documents in mono- and multilingual collections. Many additional applications of these models, however, could be enabled by more efficient techniques for processing large datasets. In this thesis, we introduce novel methods that offer efficient inference, search and evaluation for latent variable models of text. We present efficient, online inference for representing documents in several languages in a common topic space and fast …
Efficient Representation And Matching Of Texts And Images In Scanned Book Collections, Ismet Zeki Yalniz
Efficient Representation And Matching Of Texts And Images In Scanned Book Collections, Ismet Zeki Yalniz
Doctoral Dissertations
Millions of books from public libraries and private collections have been scanned by various organizations in the last decade. The motivation is to preserve the written human heritage in electronic format for durable storage and efficient access. The information buried in these large book collections has always been of major interest for scholars from various disciplines. Several interesting research problems can be defined over large collections of scanned books given their corresponding optical character recognition (OCR) outputs. At the highest level, one can view the entire collection as a whole and discover interesting contextual relationships or linkages between the books. …