Abstract Knowledge Graphs (KGs) have made a qualitative leap and effected a real
revolution in knowledge representation. This is leveraged by the underlying structure of the …

In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …

State-of-the-art sequence labeling systems traditionally require large amounts of task-
specific knowledge in the form of hand-crafted features and data pre-processing. In this …

Named entity recognition is a challenging task that has traditionally required large amounts
of knowledge in the form of feature engineering and lexicons to achieve high performance …

This paper presents an empirical study to efficiently build named entity recognition (NER)
systems when a small amount of in-domain labeled data is available. Based upon recent …

In this paper, we study Multimodal Named Entity Recognition (MNER) for social media posts.
Existing approaches for MNER mainly suffer from two drawbacks:(1) despite generating …

The representational basis for downstream natural language processing tasks is word
embeddings, which capture lexical semantics in numerical form to handle the abstract …

We propose a unified neural network architecture and learning algorithm that can be applied
to various natural language processing tasks including part-of-speech tagging, chunking …

If we take an existing supervised NLP system, a simple and general way to improve
accuracy is to use unsupervised word representations as extra word features. We evaluate …

Everyday billions of multimodal posts containing both images and text are shared in social
media sites such as Snapchat, Twitter or Instagram. This combination of image and text in a …