Text Classification

There is an abundance of text data in this world but most of it is raw. We need to extract information from this data to make use of it. One way to extract this information from raw text is to apply informative labels drawn from a pre-defined fixed set i.e. Text Classification. In this thesis, we focus on the general problem of text classification, and work towards solving challenges associated to binary/multi-class/multi-label classification. More specifically, we deal with the problem of (i) Zero-shot labels during testing; (ii) Active learning for text screening; (iii) Multi-label classification under low supervision; (iv) Structured label space; (v) Classifying pairs of words in raw text i.e. Relation Extraction. For (i), we use a zero-shot classification model that utilizes independently learned semantic embeddings. Regarding (ii), we propose a novel active learning algorithm that reduces problem of bias in naive active learning algorithms. For (iii), we propose neural candidate-selector architecture that starts from a set of high-recall candidate labels to obtain high-precision predictions. In the case of (iv), we proposed an attention based neural tree decoder that recursively decodes an abstract into the ontology tree. For (v), we propose using second-order relations that are derived by explicitly connecting pairs of words via context token(s) for improved relation extraction. We use a wide variety of both traditional and deep machine learning tools. More specifically, we used traditional machine learning models like multi-valued linear regression and logistic regression for (i, ii), deep convolutional neural networks for (iii), recurrent neural networks for (iv) and transformer networks for (v)

Riesgos de interpretación errónea en la evaluación de la Supervisión Distante para la Extracción de Relaciones

Distant Supervision is frequently used for addressing Relation Extraction. The evaluation of Distant Supervision in Relation Extraction has been attempted through Precision-Recall curves and/or calculation of Precision at N elements. However, such evaluation is challenging because the labeling of the instances results from an automatic process that can introduce noise into the labels. Consequently, the labels are not necessarily correct, affecting the learning process and the interpretation of the evaluation results. Therefore, this research aims to show that the performance of the methods measured with the mentioned evaluation strategies varies significantly if the correct labels are used during the evaluation. Besides, based on the preceding, the current interpretation of the results of these measures is questioned. To this end, we manually labeled a subset of a well-known data set and evaluated the performance of 6 traditional Distant Supervision approaches. We demonstrate quantitative differences in the evaluation scores when considering manually versus automatically labeled subsets. Consequently, the ranking of performance among distant supervision methods is different with both labeled.La Supervisión Distante se utiliza con frecuencia para abordar la extracción de relaciones. La evaluación de la Supervisión Distante en la Extracción de Relaciones se ha realizado mediante curvas de Precisión-Cobertura y/o el cálculo de la Precisión en N elementos. Sin embargo, dicha evaluación es un desafío porque el etiquetado de las instancias es el resultado de un proceso automático. En consecuencia, las etiquetas no son necesariamente correctas, afectando no solo el proceso de aprendizaje sino también la interpretación de los resultados de la evaluación. El objetivo de esta investigación es mostrar que el desempeño de los métodos medido con las estrategias de evaluación mencionadas varía de manera significativa si se utilizan las etiquetas correctas durante la evaluación. Además, basado en lo anterior, se cuestiona la interpretación actual de los resultados de estas medidas. Con este fin, etiquetamos manualmente un subconjunto de un conjunto de datos y evaluamos el desempeño de 6 enfoques tradicionales de Supervisión Distante. Demostramos diferencias cuantitativas en los puntajes de evaluación al considerar subconjuntos etiquetados manualmente versus automáticamente. En consecuencia, el orden de desempeño entre los métodos de Supervisión Distante es diferente con ambos etiquetados.The present work was supported by CONACyT/México (scholarship 937210 and grant CB-2015-01-257383). Additionally, the authors thank CONACYT for the computer resources provided through the INAOE Supercomputing Laboratory’s Deep Learning Platform for Language Technologies

Energy-Efficient Neural Network Architectures

Emerging systems for artificial intelligence (AI) are expected to rely on deep neural networks (DNNs) to achieve high accuracy for a broad variety of applications, including computer vision, robotics, and speech recognition. Due to the rapid growth of network size and depth, however, DNNs typically result in high computational costs and introduce considerable power and performance overheads. Dedicated chip architectures that implement DNNs with high energy efficiency are essential for adding intelligence to interactive edge devices, enabling them to complete increasingly sophisticated tasks by extending battery lie. They are also vital for improving performance in cloud servers that support demanding AI computations. This dissertation focuses on architectures and circuit technologies for designing energy-efficient neural network accelerators. First, a deep-learning processor is presented for achieving ultra-low power operation. Using a heterogeneous architecture that includes a low-power always-on front-end and a selectively-enabled high-performance back-end, the processor dynamically adjusts computational resources at runtime to support conditional execution in neural networks and meet performance targets with increased energy efficiency. Featuring a reconfigurable datapath and a memory architecture optimized for energy efficiency, the processor supports multilevel dynamic activation of neural network segments, performing object detection tasks with 5.3x lower energy consumption in comparison with a static execution baseline. Fabricated in 40nm CMOS, the processor test-chip dissipates 0.23mW at 5.3 fps. It demonstrates energy scalability up to 28.6 TOPS/W and can be configured to run a variety of workloads, including severely power-constrained ones such as always-on monitoring in mobile applications. To further improve the energy efficiency of the proposed heterogeneous architecture, a new charge-recovery logic family, called zero-short-circuit current (ZSCC) logic, is proposed to decrease the power consumption of the always-on front-end. By relying on dedicated circuit topologies and a four-phase clocking scheme, ZSCC operates with significantly reduced short-circuit currents, realizing order-of-magnitude power savings at relatively low clock frequencies (in the order of a few MHz). The efficiency and applicability of ZSCC is demonstrated through an ANSI S1.11 1/3 octave filter bank chip for binaural hearing aids with two microphones per ear. Fabricated in a 65nm CMOS process, this charge-recovery chip consumes 13.8µW with a 1.75MHz clock frequency, achieving 9.7x power reduction per input in comparison with a 40nm monophonic single-input chip that represents the published state of the art. The ability of ZSCC to further increase the energy efficiency of the heterogeneous neural network architecture is demonstrated through the design and evaluation of a ZSCC-based front-end. Simulation results show 17x power reduction compared with a conventional static CMOS implementation of the same architecture.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147614/1/hsiwu_1.pd

Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing