When an entity name contains other names within it, the identification of all combinations of
names can become difficult and expensive. We propose a new method to recognize not only …

Training higher-order conditional random fields is prohibitive for huge tag sets. We present
an approximated conditional random field using coarse-to-fine decoding and early updating …

The classic algorithm of Viterbi computes the most likely path in a Hidden Markov Model
(HMM) that results in a given sequence of observations. It runs in time $ O (Tn^ 2) $ given a …

A self-adaptive classifier for efficient text-stream processing is proposed. The proposed
classifier adaptively speeds up its classification while processing a given text stream for …

Accurate neural models are much less efficient than non-neural models and are useless for
processing billions of social media posts or handling user queries in real time with a limited …

Users want natural language processing (NLP) systems to be both fast and accurate, but
quality often comes at the cost of speed. The field has been manually exploring various …

The task of structured output prediction deals with learning general functional dependencies
between arbitrary input and output spaces. In this context, two loss-sensitive formulations for …

Hidden Markov models (HMMs) are characterized by an unobservable (hidden) Markov
chain and an observable process, which is a noisy version of the hidden chain. Decoding …

Sequential modeling has been widely used in a variety of important applications including
named entity recognition and shallow parsing. However, as more and more real time large …

Computer programs that make inferences about natural language are easily fooled by the
often haphazard relationship between words and their meanings. This thesis develops …