Shikhar Vashishth
Publications18
Citations79
Highly Influential Citations14
- Publications
- Influence
Open Information Extraction (OpenIE) methods extract (noun phrase, relation phrase, noun phrase) triples from text, resulting in the construction of large open Knowledge Bases (KBs). The noun phrases… Continue Reading
Distantly-supervised Relation Extraction (RE) methods train an extractor by automatically aligning relation instances in a Knowledge Base (KB) with unstructured text. In addition to relation… Continue Reading
Document date is essential for many important tasks, such as document retrieval, summarization, event detection, etc. While existing approaches for these tasks assume accurate knowledge of the… Continue Reading
Word embeddings have been widely adopted across several NLP applications. Most existing word embedding methods utilize sequential context of a word to learn its embedding. While there have been some… Continue Reading
In order to meet QoS demands from customers, currently, ISPs over-provision capacity. Networks need to continuously monitor performance metrics, such as bandwidth, packet loss etc., in order to… Continue Reading
Predicting properties of nodes in a graph is an important problem with applications in a variety of domains. Graph-based Semi Supervised Learning (SSL) methods aim to address this problem by labeling… Continue Reading
The attention layer in a neural network model provides insights into the model's reasoning behind its prediction, which are usually criticized for being opaque. Recently, seemingly contradictory… Continue Reading
Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data. However, the primary focus has been on handling simple undirected graphs.… Continue Reading
In SDN based networks, for network management such as monitoring, performance tuning, enforcing security, configurations, calculating QoS metrics etc. a certain fraction of traffic is responsible. It… Continue Reading
Recently, word embeddings have been widely adopted across several NLP applications. However, most word embedding methods solely rely on linear context and do not provide a framework for incorporating… Continue Reading