What is the relationship between sentence representations learned by deep recurrent models against those encoded by the brain?Is there any correspondence between hidden layers of these recurrent models and brain regions when processing sentences?Can these deep models be used to synthesize brain data which can then be utilized in other extrinsic tasks?We investigate these questions using sentences with simple syntax and semantics (e.g., The bone was eaten by the dog.).We consider multiple neural network architectures, including recently proposed ELMo and BERT.We use magnetoencephalography (MEG) brain recording data collected from human subjects when they were reading these simple sentences.Overall, we find that BERT's activations correlate the best with MEG brain data.We also find that the deep network representation can be used to generate brain data from new sentences to augment existing brain data.To the best of our knowledge, this is the first work showing that the MEG brain recording when reading a word in a sentence can be used to distinguish earlier words in the sentence.Our exploration is also the first to use deep neural network representations to generate synthetic brain data and to show that it helps in improving subsequent stimuli decoding task accuracy.

Word Sense Disambiguation (WSD) is a longstanding but open problem in Natural Language Processing (NLP).WSD corpora are typically small in size, owing to an expensive annotation process.Current supervised WSD methods treat senses as discrete labels and also resort to predicting the Most-Frequent-Sense (MFS) for words unseen during training.This leads to poor performance on rare and unseen senses.To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space.This allows EWISE to generalize over both seen and unseen senses, thus achieving generalized zeroshot learning.To obtain target sense embeddings, EWISE utilizes sense definitions.EWISE learns a novel sentence encoder for sense definitions by using WordNet relations and also ConvE, a recently proposed knowledge graph embedding method.We also compare EWISE against other sentence encoders pretrained on large corpora to generate definition embeddings.EWISE achieves new stateof-the-art WSD performance.

Text classification tasks suffer from curse of dimensionality due to large feature space.Short text data further exacerbates the problem due to their sparse and noisy nature.Feature selection thus becomes an important step in improving the classification performance.In this paper, we propose a novel feature selection method using Wavelet Packet Transform.Wavelet Packet Transform (WPT) has been used widely in various fields due to its efficiency in encoding transient signals.We demonstrate how short text classification task can be benefited by feature selection using WPT due to their sparse nature.Our technique chooses the most discriminating features by computing inter-class distances in the transformed space.We experimented extensively with several short text datasets.Compared to well known techniques our approach reduces the feature space size and improves the overall classification performance significantly in all the datasets.