Nathanael Chambers

45 papers receiving 2.5k citations

Hit Papers

A Corpus and Cloze Evaluation for Deeper Understanding of...20162026201920222016100200300

Peers

Nathanael Chambers
Comparison fields: 5 of 80
  • Artificial Intelligence 2.6k
  • Computer Vision and Pattern Recognition 340
  • Information Systems 321
  • Management Science and Operations Research 188
  • Molecular Biology 179
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Countries citing papers authored by Nathanael Chambers

Since Specialization
Citations

This map shows the geographic impact of Nathanael Chambers's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nathanael Chambers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nathanael Chambers more than expected).

Fields of papers citing papers by Nathanael Chambers

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nathanael Chambers. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nathanael Chambers. The network helps show where Nathanael Chambers may publish in the future.

Co-authorship network of co-authors of Nathanael Chambers

This figure shows the co-authorship network connecting the top 25 collaborators of Nathanael Chambers. A scholar is included among the top collaborators of Nathanael Chambers based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nathanael Chambers. Nathanael Chambers is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 6
3
Event Ordering with a Generalized Model for Sieve Prediction Ranking
5
4 72
5
A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Storiesbreakdown →
340
6 94
7
NavyTime: Event and Time Ordering from Raw Text
39
8
Learning the central events and participants in unlabeled text
1
9
Labeling Documents with Timestamps: Learning from their Time Expressions
23
10
Template-Based Information Extraction without the Templates
110
11
Stanford’s Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task
241
12
A Database of Narrative Schemas
18
13
Improving the Use of Pseudo-Words for Evaluating Selectional Preferences
16
14
Unsupervised Learning of Narrative Event Chains
344
15
PLOW: a collaborative task learning agent
93
16
Using semantics to identify web objects
6
17
One-Shot Procedure Learning from Instruction and Observation
7
18
Real-Time Stochastic Language Generation for Dialogue Systems
6
19
Approaches for Automatically Tagging Affect
1
20
Stochastic Language Generation in a Dialogue System: Toward a Domain Independent Generator
14

About Nathanael Chambers

Nathanael Chambers is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition, having authored 46 papers that have together received 2.8k indexed citations. Recurring topics across this work include Topic Modeling (40 papers), Natural Language Processing Techniques (36 papers) and Semantic Web and Ontologies (11 papers). The work is most often cited by research in Artificial Intelligence (2.6k citations), Computer Vision and Pattern Recognition (340 citations) and General Social Sciences (54 citations). Nathanael Chambers has collaborated with scholars based in United States, United Kingdom and Belgium. Frequent co-authors include Dan Jurafsky, James F. Allen, Yves Peirsman, Mihai Surdeanu, Heeyoung Lee, Anne Lynn S. Chang, Nasrin Mostafazadeh, Lucy Vanderwende, Steven Bethard and Bill McDowell. Their work appears in journals such as Journal of Biomedical Informatics, Computational Linguistics and Language Resources and Evaluation.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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