Deep Learning for Learning Representation and Its Application to Natural Language Processing

As the web evolves even faster than expected, the exponential growth of data becomes overwhelming. Textual data is being generated at an ever-increasing pace via emails, documents on the web, tweets, online user reviews, blogs, and so on. As the amount of unstructured text data grows, so does the need for intelligently processing and understanding it. The focus of this dissertation is on developing learning models that automatically induce representations of human language to solve higher level language tasks. In contrast to most conventional learning techniques, which employ certain shallow-structured learning architectures, deep learning is a newly developed machine learning technique which uses supervised and/or unsupervised strategies to automatically learn hierarchical representations in deep architectures and has been employed in varied tasks such as classification or regression. Deep learning was inspired by biological observations on human brain mechanisms for processing natural signals and has attracted the tremendous attention of both academia and industry in recent years due to its state-of-the-art performance in many research domains such as computer vision, speech recognition, and natural language processing. This dissertation focuses on how to represent the unstructured text data and how to model it with deep learning models in different natural language processing viii applications such as sequence tagging, sentiment analysis, semantic similarity and etc. Specifically, my dissertation addresses the following research topics: In Chapter 3, we examine one of the fundamental problems in NLP, text classification, by leveraging contextual information [MLX18a]; In Chapter 4, we propose a unified framework for generating an informative map from review corpus [MLX18b]; Chapter 5 discusses the tagging address queries in map search [Mok18]. This research was performed in collaboration with Microsoft; and In Chapter 6, we discuss an ongoing research work in the neural language sentence matching problem. We are working on extending this work to a recommendation system

Neural Models for Information Retrieval without Labeled Data

Recent developments of machine learning models, and in particular deep neural networks, have yielded significant improvements on several computer vision, natural language processing, and speech recognition tasks. Progress with information retrieval (IR) tasks has been slower, however, due to the lack of large-scale training data as well as neural network models specifically designed for effective information retrieval. In this dissertation, we address these two issues by introducing task-specific neural network architectures for a set of IR tasks and proposing novel unsupervised or \emph{weakly supervised} solutions for training the models. The proposed learning solutions do not require labeled training data. Instead, in our weak supervision approach, neural models are trained on a large set of noisy and biased training data obtained from external resources, existing models, or heuristics. We first introduce relevance-based embedding models that learn distributed representations for words and queries. We show that the learned representations can be effectively employed for a set of IR tasks, including query expansion, pseudo-relevance feedback, and query classification. We further propose a standalone learning to rank model based on deep neural networks. Our model learns a sparse representation for queries and documents. This enables us to perform efficient retrieval by constructing an inverted index in the learned semantic space. Our model outperforms state-of-the-art retrieval models, while performing as efficiently as term matching retrieval models. We additionally propose a neural network framework for predicting the performance of a retrieval model for a given query. Inspired by existing query performance prediction models, our framework integrates several information sources, such as retrieval score distribution and term distribution in the top retrieved documents. This leads to state-of-the-art results for the performance prediction task on various standard collections. We finally bridge the gap between retrieval and recommendation models, as the two key components in most information systems. Search and recommendation often share the same goal: helping people get the information they need at the right time. Therefore, joint modeling and optimization of search engines and recommender systems could potentially benefit both systems. In more detail, we introduce a retrieval model that is trained using user-item interaction (e.g., recommendation data), with no need to query-document relevance information for training. Our solutions and findings in this dissertation smooth the path towards learning efficient and effective models for various information retrieval and related tasks, especially when large-scale training data is not available

Region-Based Image Retrieval Revisited

Region-based image retrieval (RBIR) technique is revisited. In early attempts at RBIR in the late 90s, researchers found many ways to specify region-based queries and spatial relationships; however, the way to characterize the regions, such as by using color histograms, were very poor at that time. Here, we revisit RBIR by incorporating semantic specification of objects and intuitive specification of spatial relationships. Our contributions are the following. First, to support multiple aspects of semantic object specification (category, instance, and attribute), we propose a multitask CNN feature that allows us to use deep learning technique and to jointly handle multi-aspect object specification. Second, to help users specify spatial relationships among objects in an intuitive way, we propose recommendation techniques of spatial relationships. In particular, by mining the search results, a system can recommend feasible spatial relationships among the objects. The system also can recommend likely spatial relationships by assigned object category names based on language prior. Moreover, object-level inverted indexing supports very fast shortlist generation, and re-ranking based on spatial constraints provides users with instant RBIR experiences.Comment: To appear in ACM Multimedia 2017 (Oral