Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Radev, Dragomir R."'
Autor:
Yasunaga, Michihiro, Kasai, Jungo, Zhang, Rui, Fabbri, Alexander R., Li, Irene, Friedman, Dan, Radev, Dragomir R.
Scientific article summarization is challenging: large, annotated corpora are not available, and the summary should ideally include the article's impacts on research community. This paper provides novel solutions to these two challenges. We 1) develo
Externí odkaz:
http://arxiv.org/abs/1909.01716
Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly. Single document summarization (SDS) systems have benefited from advances in neural encoder-decoder model thanks to th
Externí odkaz:
http://arxiv.org/abs/1906.01749
What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning
Recent years have witnessed the rising popularity of Natural Language Processing (NLP) and related fields such as Artificial Intelligence (AI) and Machine Learning (ML). Many online courses and resources are available even for those without a strong
Externí odkaz:
http://arxiv.org/abs/1811.12181
While neural networks have shown impressive performance on large datasets, applying these models to tasks where little data is available remains a challenging problem. In this paper we propose to use feature transfer in a zero-shot experimental setti
Externí odkaz:
http://arxiv.org/abs/1808.09889
Autor:
Fabbri, Alexander R., Li, Irene, Trairatvorakul, Prawat, He, Yijiao, Ting, Wei Tai, Tung, Robert, Westerfield, Caitlin, Radev, Dragomir R.
The field of Natural Language Processing (NLP) is growing rapidly, with new research published daily along with an abundance of tutorials, codebases and other online resources. In order to learn this dynamic field or stay up-to-date on the latest res
Externí odkaz:
http://arxiv.org/abs/1805.04617
Autor:
Qazvinian, Vahed, Radev, Dragomir R., Mohammad, Saif M., Dorr, Bonnie, Zajic, David, Whidby, Michael, Moon, Taesun
Publikováno v:
Journal Of Artificial Intelligence Research, Volume 46, pages 165-201, 2013
Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material. Our goal is to effectively serve this need by using bibliometric text mining and summarization techniques to
Externí odkaz:
http://arxiv.org/abs/1402.0556
Autor:
Qazvinian, Vahed, Radev, Dragomir R.
This paper is focused on the computational analysis of collective discourse, a collective behavior seen in non-expert content contributions in online social media. We collect and analyze a wide range of real-world collective discourse datasets from m
Externí odkaz:
http://arxiv.org/abs/1204.3498
Autor:
Erkan, Gunes, Radev, Dragomir R.
Publikováno v:
Journal Of Artificial Intelligence Research, Volume 22, pages 457-479, 2004
We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience
Externí odkaz:
http://arxiv.org/abs/1109.2128
Autor:
Qazvinian, Vahed, Radev, Dragomir R.
Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study. One possible way to overcome this problem is to summarize a scientific topic. In this paper, we propose a mode
Externí odkaz:
http://arxiv.org/abs/0807.1560
Publikováno v:
ANLP'00, Seattle, WA, May 2000
In this paper, we describe a system to rank suspected answers to natural language questions. We process both corpus and query using a new technique, predictive annotation, which augments phrases in texts with labels anticipating their being targets o
Externí odkaz:
http://arxiv.org/abs/cs/0005029