Tutorial: Machine Learning Methods in Natural Language Processing

Abstract

Statistical or machine learning approaches have become quite prominent in the Natural Language Processing literature. Common techniques include generative models such as Hidden Markov Models or Probabilistic Context-Free Grammars, and more general noisy-channel models such as the statistical approach to machine translation pioneered by researchers at IBM in the early 90s. Recent work has considered discriminative methods such as (conditional) markov random fields, or large-margin methods. This tutorial will describe several of these techniques. The methods will be motivated through a number of natural language problems: from part-of-speech tagging and parsing, to machine translation, dialogue systems and information extraction problems. I will also concentrate on links to the COLT and kernel methods literature: for example covering kernels over the discrete structures found in NLP, online algorithms for NLP problems, and the issues in extending generalization bounds from classification problems to NLP problems such as parsing.

Pragmatic approach for Twitter analysis and perform prediction

“…Michael Collins et al [3] purpose use Machine Learning Methods in language Processing like Information extraction Named entities, Relationships between entities, finding linguistic structure, Check Word functions in meaning still as grammatically within the sentence, and computational linguistics uses Perceptron Algorithm and solving Problem of Pragmatic Ambiguity.…”

From narrative descriptions to MedDRA: automagically encoding adverse drug reactions

“…Other related papers about pharmacovigilance and machine learning or data mining are [18,19]. In [20], a text extraction tool is implemented on the .NET platform for preprocessing text (removal of stop words, Porter stemming [21] and use of synonyms) and matching medical terms using permutations of words and spelling variations (Soundex, Levenshtein distance and Longest common subsequence distance [22]). Its performance has been evaluated on both manually extracted medical terms from summaries of product characteristics and unstructured adverse effect texts from Martindale (a medical reference for information about drugs and medicines) using the WHO-ART and MedDRA medical terminologies.…”

Automagically Encoding Adverse Drug Reactions in MedDRA

“…Other interesting papers about pharmacovigilance and machine learning or data mining are, e.g., [14] and [15]. In [16] a text extraction tool is implemented on the .NET platform with functionalities for preprocessing text (removal of stop words, Porter stemming and use of synonyms) and matching medical terms using permutations of words and spelling [17]). Its performance has been evaluated on both manually extracted medical terms from summaries of product characteristics and unstructured adverse effect texts from Martindale (i.e.…”