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active learning

Simple, Fast, Effective, Active Learning

Recently, we “read” ten thousand recipes or so from a cooking web site.  The purpose of doing so was to produce a formal representation of those recipes for use in temporal reasoning by a robot.

Our task was to produce ontology by reading the recipes subject to conflicting goals.  On the one hand, the ontology was to be accurate so that the robot could reason, plan, and answer questions robustly.  On the other hand, the ontology was to be produced automatically (with minimal human effort).[1]

In order to minimize human effort while still obtaining deep parses from which we produce ontology, we used more techniques from statistical natural language processing than we typically do in knowledge acquisition for deep QA, compliance, or policy automation.  (Consider that NLP typically achieves less than 90% syntactic accuracy while such work demands near 100% semantic accuracy.)[2]

In the effort, we refined some prior work on representing words as vectors and semi-supervised learning.  In particular, we adapted semi-supervised, active learning similar to Stratos & Collins 2015 using enhancements to the canonical correlation analysis (CCA) of Dhillon et al 2015 to obtain accurate part of speech tagging, as conveyed in the following graphic from Stratos & Collins:


Of Kalman Filters and Hidden Markov Models

This provides some background relating to some work we did on part of speech tagging for a modest, domain-specific corpus.  The path is from Hsu et al 2012, which discusses spectral methods based on singular value decomposition (SVD) as a better method for learning hidden Markov models (HMM) and the use of word vectors instead of clustering to improve aspects of NLP, such as part of speech tagging.

The use of vector representations of words for machine learning in natural language is not all that new.  A quarter century ago, Brown et al 1992 introduced hierarchical agglomerative clustering of words based on mutual information and the use of those clusters within hidden Markov language models.  One notable difference versus today’s word vectors is that paths through the hierarchy of clusters to words at the leaves correspond to vectors of bits (i.e., Boolean features) rather than real-valued features.