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Deep Learning for NLP (without Magic) Richard Socher, Yoshua Bengio and Chris Manning ACL 2012 Deep Learning NER WordNet Most current machine learning works well because of human-‐designed SRL Parser representa>ons and input features Machine learning becomes just op>mizing weights to best make a final predic>on Representa>on learning aCempts to automa>cally learn good features or representa>ons Deep learning algorithms aCempt to learn mul>ple levels of representa>on of increasing complexity/abstrac>on 2 A Deep Architecture Mainly, work has explored deep belief networks (DBNs), Markov Random Fields with mul>ple layers, and various types of mul>ple-‐layer neural networks Output layer Here predic>ng a supervised target Hidden layers These learn more abstract representa>ons as you head up Input layer Raw sensory inputs (roughly) 3 Part 1.1: The Basics Five Reasons to Explore Deep Learning 4 #1 Learning representations Handcra\ing features is >me-‐consuming The features are o\en both over-‐specified and incomplete The work has to be done again for each task/domain/… We must move beyond handcra\ed features and simple ML Humans develop representa>ons for learning and reasoning Our computers should do the same Deep learning provides a way of doing this 5 #2 The need for distributed representations Current NLP systems are incredibly fragile because of their atomic symbol representa>ons Crazy sentenAal complement, such as for “likes [(being) crazy]” 6 #2 The need for distributed representations Learned word representa>ons that model similari>es help enormously in NLP Distribu>onal similarity based word clusters greatly help most applica>ons +1.4% F1 Dependency Parsing 15.2% error reduc>on (Koo & Collins 2008, Brown clustering) +3.4% F1 Named En>ty Recogni>on 23.7% error reduc>on (Stanford NER, exchange clustering) 7 #2 The need for distributed C1 C2 C3 representations Mul>-‐ input Clustering Clustering Learning features that are not mutually exclusive can be exponen>ally more efficient than nearest-‐neighbor-‐like or clustering-‐like models 8 Distributed representations deal with the curse of dimensionality Generalizing locally (e.g., nearest neighbors) requires representa>ve examples for all relevant varia>ons! Classic solu>ons: •  Manual feature design •  Assuming a smooth target func>on (e.g., linear models) •  Kernel methods (linear in terms of kernel based on data points) Neural networks parameterize and learn a “similarity” kernel 9 #3 Unsupervised feature and weight learning Today, most prac>cal, good NLP& ML methods require labeled training data (i.e., supervised learning) But almost all data is unlabeled Most informa>on must be acquired unsupervised Fortunately, a good model of observed data can really help you learn classifica>on decisions 10

Deep Learning for NLP - The Stanford NLP PDF

186 Pages·2012·24.77 MB·English

by Christopher Manning

#nlp #deep learning

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by Christopher Manning| 2012| 186 pages| 24.77| English

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Author:	Christopher Manning
Publication Year:	2012
Pages:	186
Language:	English
File Size:	24.77
Format:	PDF
Price:	FREE

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