Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Chandrasekaran, Muthu Kumar"'
Autor:
Zhao, Chao, Huang, Tenghao, Chowdhury, Somnath Basu Roy, Chandrasekaran, Muthu Kumar, McKeown, Kathleen, Chaturvedi, Snigdha
A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document. However, this method neglects the relative importance of docum
Externí odkaz:
http://arxiv.org/abs/2203.10254
Autor:
Saravanakumar, Kailash Karthik, Ballesteros, Miguel, Chandrasekaran, Muthu Kumar, McKeown, Kathleen
We propose a method for online news stream clustering that is a variant of the non-parametric streaming K-means algorithm. Our model uses a combination of sparse and dense document representations, aggregates document-cluster similarity along these m
Externí odkaz:
http://arxiv.org/abs/2101.11059
Autor:
Jaidka, Kokil, Yasunaga, Michihiro, Chandrasekaran, Muthu Kumar, Radev, Dragomir, Kan, Min-Yen
This overview describes the official results of the CL-SciSumm Shared Task 2018 -- the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain. This year, the dataset comprised 60 annotated set
Externí odkaz:
http://arxiv.org/abs/1909.00764
Autor:
Chandrasekaran, Muthu Kumar, Yasunaga, Michihiro, Radev, Dragomir, Freitag, Dayne, Kan, Min-Yen
The CL-SciSumm Shared Task is the first medium-scale shared task on scientific document summarization in the computational linguistics~(CL) domain. In 2019, it comprised three tasks: (1A) identifying relationships between citing documents and the ref
Externí odkaz:
http://arxiv.org/abs/1907.09854
Due to time constraints, course instructors often need to selectively participate in student discussion threads, due to their limited bandwidth and lopsided student--instructor ratio on online forums. We propose the first deep learning models for thi
Externí odkaz:
http://arxiv.org/abs/1905.10851
The $3^{rd}$ joint BIRNDL workshop was held at the 41st ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018) in Ann Arbor, USA. BIRNDL 2018 intended to stimulate IR researchers and digital library professionals to el
Externí odkaz:
http://arxiv.org/abs/1812.00427
The large scale of scholarly publications poses a challenge for scholars in information seeking and sensemaking. Bibliometrics, information retrieval (IR), text mining and NLP techniques could help in these search and look-up activities, but are not
Externí odkaz:
http://arxiv.org/abs/1706.02509
We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs). Our key finding is that using automatically obtained discourse relations improves the prediction of when instructors i
Externí odkaz:
http://arxiv.org/abs/1612.00944
Publikováno v:
Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications, pages 30 to 39, Osaka, Japan, December 12 2016
Word embeddings are now ubiquitous forms of word representation in natural language processing. There have been applications of word embeddings for monolingual word sense disambiguation (WSD) in English, but few comparisons have been done. This paper
Externí odkaz:
http://arxiv.org/abs/1611.02956
With large student enrollment, MOOC instructors face the unique challenge in deciding when to intervene in forum discussions with their limited bandwidth. We study this problem of instructor intervention. Using a large sample of forum data culled fro
Externí odkaz:
http://arxiv.org/abs/1504.07206