Filter Results:
- Full text PDF available (37)
Publication Year
2000
2017
- This year (2)
- Last 5 years (10)
- Last 10 years (26)
Publication Type
Co-author
Journals and Conferences
Data Set Used
Learn More
A significant portion of the world’s text is tagged by readers on social bookmarking websites. Credit attribution is an inherent problem in these corpora because most pages have multiple tags, but… (More)
- Mihai Surdeanu, Julie Tibshirani, Ramesh Nallapati, Christopher D. Manning
- EMNLP-CoNLL
- 2012
Distant supervision for relation extraction (RE) – gathering training data by aligning a database of facts with text – is an efficient approach to scale RE to thousands of different relations.… (More)
In this work, we model abstractive text summarization using Attentional EncoderDecoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora. We… (More)
- Ramesh Nallapati
- SIGIR
- 2004
Discriminative models have been preferred over generative models in many machine learning problems in the recent past owing to some of their attractive theoretical properties. In this paper, we… (More)
In this work, we address the problem of joint modeling of text and citations in the topic modeling framework. We present two different models called the Pairwise-Link-LDA and the Link-PLSA-LDA… (More)
- Ramesh Nallapati, Ao Feng, Fuchun Peng, James R Allan
- CIKM
- 2004
With the overwhelming volume of online news available today, there is an increasing need for automatic techniques to analyze and present news to the user in a meaningful and efficient manner.… (More)
- Andrew Arnold, Ramesh Nallapati, William W. Cohen
- Seventh IEEE International Conference on Data…
- 2007
The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research. While previous work has… (More)
The problem of rare and unknown words is an important issue that can potentially effect the performance of many NLP systems, including both the traditional countbased and the deep learning models. We… (More)
- Ramesh Nallapati, Susan Ditmore, John D. Lafferty, Kin Ung
- KDD
- 2007
Modeling the evolution of topics with time is of great value in automatic summarization and analysis of large document collections. In this work, we propose a new probabilistic graphical model to… (More)
- Ramesh Nallapati, Feifei Zhai, Bowen Zhou
- AAAI
- 2017
We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to… (More)