# Semantic Relation Classification——via Convolution Neural Network

## Presented by

Rui Gong, Xinqi Ling, Di Ma,Xuetong Wang

## Introduction

One of the emerging trends of natural language technologies is their use for the humanities and sciences (Gbor et al., 2018). SemEval 2018 Task 7 mainly solves the problem of relation extraction and classification of two entities in the same sentence into 6 potential relations. The 6 relations are USAGE, RESULT, MODEL-FEATURE,PART WHOLE, TOPIC, and COMPARE.

Data comes from 350 scientific paper abstracts, which have 1228 and 1248 annotated sentences for two tasks. For each data, an example sentence was chosen with its right and left sentences, as well as an indicator showing whether the relation is reserved, then a prediction is made.

Three models were used for the prediction: Linear Classifiers, Long Short-Term Memory(LSTM), and Convolutional Neural Network.

## Algorithm

After featurizing all words in the sentence. The sentence of length N can be expressed as a vector of length [math] N [/math], which looks like $$e=[e_{1},e_{2},\ldots,e_{N}]$$ and each entry represents a token of the word. Also, to apply convolutional neural network, the subsets of features $$e_{i:i+j}=[e_{i},e_{i+1},\ldots,e_{i+j}]$$ is given to a weight matrix [math] W\in\mathbb{R}^{(d^{w}+2d^{wp})\times k}[/math] to produce a new feature, defiend as $$c_{i}=tanh(W\cdot e_{i:i+k-1}+bias)$$ This process is applied to all subsets of features with length [math] k [/math] starting from the first one. Then a mapped feature factor $$c=[c_{1},c_{2},\ldots,c_{N-k+1}]$$ is produced.

The max pooling operation is used, the [math] \hat{c}=max\{c\} [/math] was picked. With different weight filter, different mapped feature vectors can be obtained. Finally, the original sentence [math] e [/math] can be converted into a new representation [math] r_{x} [/math] with a fixed length. For example, if there are 5 filters, then there are 5 features ([math] \hat{c} [/math]) picked to create [math] r_{x} [/math] for each [math] x [/math].

Then, the score vector $$s(x)=W^{classes}r_{x}$$ is obtained which represented the score for each class, given [math] x [/math]'s entities' relation will be classified as the one with the highest score. The [math] W^{classes} [/math] here is the model being trained.

To improve the performance, “Negative Sampling" was used. Given the trained data point [math] \tilde{x} [/math], and its correct class [math] \tilde{y} [/math]. Let [math] I=Y\setminus\{\tilde{y}\} [/math] represent the incorrect labels for [math] x [/math]. Basically, the distance between the correct score and the positive margin, and the negative distance (negative margin plus the second largest score) should be minimized. So the loss function is $$L=log(1+e^{\gamma(m^{+}-s(x)_{y})}+log(1+e^{\gamma(m^{-}-\mathtt{max}_{y'\in I}(s(x)_{y'}))}$$ with margins [math] m_{+} [/math], [math] m_{-} [/math], and penalty scale factor [math] \gamma [/math]. The whole training is based on ACL anthology corpus and there are 25,938 papers with 136,772,370 tokens in total, and 49,600 of them are unique.

## Conclusions

Throughout the process, linear classifiers, sequential random forest, LSTM and CNN models are tested. Variations are applied to the models. Among all variations, vanilla CNN with negative sampling and ACL-embedding has significant better performance than all others. Attention based pooling, up-sampling and data augmentation are also tested, but they barely perform positive incresement on the behaviour.