arXiv subjects

Vivek Gupta

Publications and source records attributed to Vivek Gupta.

132 records · Page 8Linked to original sources

arXiv2017

Leveraging Distributional Semantics for Multi-Label Learning

We present a novel and scalable label embedding framework for large-scale multi-label learning a.k.a ExMLDS (Extreme Multi-Label Learning using Distributional Semantics). Our approach draws inspiration from ideas rooted in distributional semantics, specifically the Skip Gram Negative Sampling (SGNS) approach, widely used to learn word embeddings for natural language processing tasks. Learning such embeddings can be reduced to a certain matrix factorization. Our approach is novel in that it highlights interesting connections between label embedding methods used for multi-label learning and paragraph/document embedding methods commonly used for learning representations of text data. The framework can also be easily extended to incorporate auxiliary information such as label-label correlations; this is crucial especially when there are a lot of missing labels in the training data. We demonstrate the effectiveness of our approach through an extensive set of experiments on a variety of benchmark datasets, and show that the proposed learning methods perform favorably compared to several baselines and state-of-the-art methods for large-scale multi-label learning. To facilitate end-to-end learning, we develop a joint learning algorithm that can learn the embeddings as well as a regression model that predicts these embeddings given input features, via efficient gradient-based methods.

cs.LG↗

arXiv2017

Text Summarization using Abstract Meaning Representation

With an ever increasing size of text present on the Internet, automatic summary generation remains an important problem for natural language understanding. In this work we explore a novel full-fledged pipeline for text summarization with an intermediate step of Abstract Meaning Representation (AMR). The pipeline proposed by us first generates an AMR graph of an input story, through which it extracts a summary graph and finally, generate summary sentences from this summary graph. Our proposed method achieves state-of-the-art results compared to the other text summarization routines based on AMR. We also point out some significant problems in the existing evaluation methods, which make them unsuitable for evaluating summary quality.

cs.CL↗

arXiv2017

SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations

We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation. In SCDV, word embedding's are clustered to capture multiple semantic contexts in which words occur. They are then chained together to form document topic-vectors that can express complex, multi-topic documents. Through extensive experiments on multi-class and multi-label classification tasks, we outperform the previous state-of-the-art method, NTSG (Liu et al., 2015a). We also show that SCDV embedding's perform well on heterogeneous tasks like Topic Coherence, context-sensitive Learning and Information Retrieval. Moreover, we achieve significant reduction in training and prediction times compared to other representation methods. SCDV achieves best of both worlds - better performance with lower time and space complexity.

cs.CL↗

arXiv2017

User Bias Removal in Review Score Prediction

Review score prediction of text reviews has recently gained a lot of attention in recommendation systems. A major problem in models for review score prediction is the presence of noise due to user-bias in review scores. We propose two simple statistical methods to remove such noise and improve review score prediction. Compared to other methods that use multiple classifiers, one for each user, our model uses a single global classifier to predict review scores. We empirically evaluate our methods on two major categories (\textit{Electronics} and \textit{Movies and TV}) of the SNAP published Amazon e-Commerce Reviews data-set and Amazon \textit{Fine Food} reviews data-set. We obtain improved review score prediction for three commonly used text feature representations.

cs.CL↗

arXiv2016

Assisting humans to achieve optimal sleep by changing ambient temperature

Environment plays a vital role in the sleep mechanism of a human. It has been shown from many studies that sleeping and waking environment, waking time and hours of sleep is of very significant importance which can result in sleeping disorders and variety of diseases. This paper finds the sleep cycle of an individual and according changes the ambient temperature to maximize his/her sleep efficiency. We suggest a method which will assist in increasing sleep efficiency. Using Fast-Fourier-Transformation (FFT) of heart rate signals to extract heart rate variability data such that low frequency / high frequency (LF/HF) power ratio we are detecting sleep stages using an automated algorithm and then applying feedback mechanism to alter the ambient temperature depending upon the sleep stage.

cs.CY↗

arXiv2016

Product Classification in E-Commerce using Distributional Semantics

Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction. To address the above challenges, we propose a new distributional semantics representation for document vector formation. We also develop a new two-level ensemble approach utilizing (with respect to the taxonomy tree) a path-wise, node-wise and depth-wise classifiers for error reduction in the final product classification. Our experiments show the effectiveness of the distributional representation and the ensemble approach on data sets from a leading e-commerce platform and achieve better results on various evaluation metrics compared to earlier approaches.

cs.AI↗