August 6, 2019 /
Jesica Brierly
/ 1 Comment

Hello, and welcome! In this video, we’ll provide an overview of

Recursive Neural Tensor Networks, as well as the natural language processing problems

that they’re able to solve. Sentiment Analysis is the task of identifying

and extracting subjective information, like emotion or opinion, from a source material. For example, this might involve analyzing

a twitter feed to determine which tweets express a positive feeling, which express a negative

feeling, and which are neutral. In order to classify sentences into different

sentiment classes, we’ll need a dataset to use for training. One potential dataset is the Stanford Sentiment

Treebank. Each data point is the syntax tree of a rotten

tomatoes review. The tree itself and all the subtrees are labeled

with a sentiment value from 1 to 25. 25 is the best possible review, while 1 is

the worst. The dataset was created by Stanford researchers,

who utilized Amazon’s Mechanical Turk platform in order to assign values. Recursive neural models can be used for the

sentiment analysis problem. These types of models are characterized by

their use of vector representations. Vectors are used to represent words, as well

as all sub-sentences related to an input’s syntax tree. The word representations are trained with

the model, and the representations of sub-sentences are calculated with a compositionality function. To calculate the sub-sentence’s representations,

we apply the compositionality function bottom-up according to the input’s parse tree. All vectors are fed to the same softmax classifier

to determine the sentiment. The choice of compositionality function is

important, so we’ll present three different types of recursive models, each with a different

function. The first model we’ll look at is the basic

Recursive Neural Network. To compute our word composition, we start

with our vectors that we want to combine, which we’ll call “b” and “c”. We form a “two d” by “d” matrix by concatenating “b” and “c”. This new matrix is multiplied by the “d” by

“two d” weight matrix “W”. “W” is the model’s main training parameter. Then a nonlinearity is applied element-wise

to the resulting vector. In this case, the nonlinearity is the hyperbolic

tangent function. As a brief note, we?ve omitted the bias for

simplicity. Other models use this compositionality function,

like the recursive autoencoder, and recursive auto-associative memories. As you can see, the words only interact implicitly

through the nonlinearity, so the compositionality function may not be consistent with linguistic

principles. The model also ignores reconstruction loss,

since the dataset is large enough to compensate. Now let’s move on to Matrix-Vector Recursive

Neural Networks. This type of model is a linguistically-motivated

improvement over the basic recursive neural network. The big change is that now every word is represented

by both a vector and a “d” by “d” matrix. The compositionality function that you see

here takes four objects. Lowercase “b” and “c” are the word vectors,

while the uppercase “b” and “c” are the respective matrices. Lowercase “p1” is the resulting vector, while

uppercase “P1” is the respective matrix. Just like with basic recursive neural networks,

a matrix “W” is multiplied with a matrix created from the word’s representations. But in this case, the matrix created is much

more dependent on the relationship between the two input words. The problem with this model is that the number

of trainable parameters becomes too large as the vocabulary size increases. The Recursive Neural Tensor Network, or RNTN,

uses a powerful fixed-size compositionality function that only takes the word’s vectors

as arguments. The model is not parameterized by matrices

but it adds a “two d” by “d” by “d” tensor that is used in the function. This tensor is also trained with the model. Each of the “d” slices captures a different type of composition,

so intuitively, it is more capable of learning than the basic recursive neural network. It turns out that RNTNs outperform the known

alternative methods. It has achieved over eighty-seven percent

accuracy in positive negative word classification, and over eighty-five percent accuracy in positive

negative sentence classification on the Stanford Sentiment Treebank. This is a sentence classification accuracy

that’s more than three percent higher compared to normal Recurrent Networks. Recursive Neural Tensor Networks can also

be used in other applications, such as Parsing Natural scenes, and Parsing Natural languages. This is due to the recursive nature of these

problems. If you’re interested in learning more about

RNTNs, we recommend you follow the link here to a great article by Socher, and others. By now, you should understand the intuition

behind recursive neural models, and recursive neural tensor networks. Thank you for watching this video.

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## 1 Comment

great examples, very intuitive!