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Sentence Modeling• Representation of sentences is the heart of Natural Language
Processing • A sentence model is a representation and analysis of semantic
content of a sentence for classification or generation• The sentence modeling task is at the core of many tasks such as
sentiment analysis, paraphrase detection, entailment recognition, summarization, discourse analysis, machine translation, grounded language learning and image retrieval• The aim of sentence modeling is a feature function that guides the
process by which features of a sentence are extracted.
One Dimensional Convolution
Vector of weights Size: m Filter
Input SequenceSentence
M-gram Dot product
Produces a sequence c
Narrow ConvolutionSize of c :
s – m + 1
It requires thats ≥ m
Wide Convolution
Size:s + m - 1
Wide Convolution• Size of c
s+m-1
• No requirement on s or m• Out of range values are taken to be 0• Result of narrow convolution is subsequence of result of wide
convolution
Advantages of Wide Convolution• Guarantees that a valid non empty c will always be produced• All weights in the filter reach the entire sentence• Holds no limit on the size of m or s
Time Delay Neural Network• A key feature for TDNN’s are the ability to express a relation between
inputs in time.• The sequence s is viewed as having a time dimension and the
convolution is applied over the time dimension.
Max TDNN
Properties of Max TDNN• Sensitive to order of the words• Does not depend on external language specific features• Largely uniform importance to the signal from each of the words
• Range of feature detectors is limited• Higher order and long range feature detectors cannot be incorporated• Multiple occurrences of features and the sequence ignored• Pooling factor: s-m+1
k-Max Pooling
k – Max Pooling• Given a value k and a sequence P of length p, k-max pooling selects
the subsequence p-max of the k highest values of p.
• The order of the values in p-max corresponds to the original order in p.
k-Max Pooling• k most active features• Features may be number of positions apart• Preserves the order of the features• But is insensitive to their specific positions• Can detect multiple occurrences of feature
What should k be?• Why not let it decide for itself?
Dynamic k-Max Pooling
Suppose length of sentence = 18L = 3Ktop = 3
K1 = 12K2 = 6K3 = 3
Multiple Feature Maps
Convolution K-max Pooling Layer
Non linear function
Feature Map
Second Order
Feature Map
To increase the number of learnt feature detectors of a certain order, multiple feature maps may be computed in parallel at the same layer.
Folding• Feature detectors in different rows are independent of each other.
Properties of Sentence Model• The subsequence of n-grams extracted by the pooling operation
induces invariance to absolute positions, but maintains their order and relative positions.
• DCNN feature graphs have a global range of the pooling operations
• DCNN has internal input dependent structure and does not rely on externally provided parse trees.
Experiments
Sentiment Prediction in Movie Reviews• Concerns prediction of sentiment of movie reviews in Stanford
Sentiment Treebank• Output is binary in experiment 1 and “negative, somewhat negative,
neutral, somewhat positive, positive” in experiment 2• Binary: MultiClass:
Question Type Classification• TREC question dataset• Six Different Question Types
Twitter Sentiment Prediction with Distant Supervision• Large dataset of tweets• Tweet is labelled positive or negative automatically based on
emoticon• Tweets are preprocessed
Conclusion• Dynamic CNN defined, which uses Dynamic k-max Pooling• Feature Graph captures word relation of varying size• High performance on sentiment prediction and question classification
without requiring external features