Over the past few years, neural networks have re-emerged as powerful machine-learning
models, yielding state-of-the-art results in fields such as image recognition and speech …

Neural networks are a family of powerful machine learning models and this book focuses on
their application to natural language data. The first half of the book (Parts I and II) covers the …

Click-through rate (CTR) prediction plays an important role in computational advertising.
Models based on degree-2 polynomial map**s and factorization machines (FMs) are …

Kernel techniques have long been used in SVM to handle linearly inseparable problems by
transforming data to a high dimensional space, but training and testing large data sets is …

We present Searn, an algorithm for integrating sear ch and l earn ing to solve complex
structured prediction problems such as those that occur in natural language, speech …

In this paper, we provide a study on the use of tree kernels to encode syntactic parsing
information in natural language learning. In particular, we propose a new convolution kernel …

We present an approach to using a morphological analyzer for tokenizing and
morphologically tagging (including partof-speech tagging) Arabic words in one process. We …

Quantitative data is important in many domains. Information extraction methods draw
structured data from documents. However, the extraction of quantities and their contexts has …

Personalized recommendation of Points of Interest (POIs) plays a key role in satisfying users
on Location-Based Social Networks (LBSNs). In this article, we propose a probabilistic …

Solutions for learning from large scale datasets, including kernel learning algorithms that
scale linearly with the volume of the data and experiments carried out on realistically large …