Nearly-Unsupervised Hashcode Representations for Relation Extraction

In a very recent work, kernelized locality sensitive hashcodes based representation learning approach has been proposed that has shown to be the most successful in terms of accuracy and computational efficiency for the task (Garg et al., 2019). The model parameters, shared between all the hash functions, are optimized in a supervised manner, whereas an individual hash function is constructed in a randomized fashion. The authors suggest to obtain thousands of (randomized) semantic features extracted from natural language data points into binary hashcodes, and then making classification decision as per the features using hundreds of decision trees, which is the core of their robust classification approach. Even if we extract thousands of semantic features using the hashing approach, it is difficult to ensure that the features extracted from training data points would generalize to a test set. While the inherent randomness in constructarXiv:1909.03881v1 [cs.LG] 9 Sep 2019 Figure 1: On the left, we show an abstract meaning representation (AMR) of a sentence. As per the semantics of the sentence, there is a valid biomedical relationship between the two proteins, Ras and Raf, i.e. Ras catalyzes phosphorylation of Raf; the relation corresponds to a subgraph extracted from the AMR. On the other hand, one of the many invalid biomedical relationships that one could infer is, Ras catalyzes activation of Raf, for which we show the corresponding subgraph too. A given candidate relation automatically hypothesized from the sentence, is binary classified, as valid or invalid, using the subgraph as features.