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=== Recursive Autoencoders ===
Paraphrase recognition has been attempted by Socher et al<ref name=Socher>{{Citation |last1=Socher |first1=Richard |last2=Huang |first2=Eric |last3=Pennington |first3=Jeffrey |last4=Ng |first4=Andrew |last5=Manning |first5=Christopher |title=Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection |chapter=Advances in Neural Information Processing Systems 24 |year=2011 |chapter-url=http://www.socher.org/index.php/Main/DynamicPoolingAndUnfoldingRecursiveAutoencodersForParaphraseDetection |access-date=2017-12-29 |archive-date=2018-01-06 |archive-url=https://web.archive.org/web/20180106173348/http://www.socher.org/index.php/Main/DynamicPoolingAndUnfoldingRecursiveAutoencodersForParaphraseDetection |url-status=dead }}</ref> through the use of recursive [[autoencoder]]s. The main concept is to produce a vector representation of a sentence and its components by recursively using an autoencoder. The vector representations of paraphrases should have similar vector representations; they are processed, then fed as input into a [[artificial neural network|neural network]] for classification.
Given a sentence <math>W</math> with <math>m</math> words, the autoencoder is designed to take 2 <math>n</math>-dimensional [[word embedding]]s as input and produce an <math>n</math>-dimensional vector as output. The same autoencoder is applied to every pair of words in <math>S</math> to produce <math>\lfloor m/2 \rfloor</math> vectors. The autoencoder is then applied recursively with the new vectors as inputs until a single vector is produced. Given an odd number of inputs, the first vector is forwarded as-is to the next level of recursion. The autoencoder is trained to reproduce every vector in the full recursion tree, including the initial word embeddings.
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