Ralph Grishman
About
In The Last Decade
Ralph Grishman
235 papers receiving 7.5k citations
Hit Papers
Peers
Comparison fields: 5 of 142
- Artificial Intelligence 7.9k
- Information Systems 1.6k
- Molecular Biology 863
- Management Science and Operations Research 767
- Computer Vision and Pattern Recognition 417
Countries citing papers authored by Ralph Grishman
This map shows the geographic impact of Ralph Grishman'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 Ralph Grishman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ralph Grishman more than expected).
Fields of papers citing papers by Ralph Grishman
This network shows the impact of papers produced by Ralph Grishman. 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 Ralph Grishman. The network helps show where Ralph Grishman may publish in the future.
Co-authorship network of co-authors of Ralph Grishman
This figure shows the co-authorship network connecting the top 25 collaborators of Ralph Grishman. A scholar is included among the top collaborators of Ralph Grishman 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 Ralph Grishman. Ralph Grishman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Joint Event Extraction via Recurrent Neural Networks breakdown → | 380 |
| 2 | New York University 2016 System for KBP Event Nugget: A Deep Learning Approach. | 32 |
| 3 | Relation Extraction: Perspective from Convolutional Neural Networks breakdown → | 291 |
| 4 | Improving Event Detection with Active Learning | 12 |
| 5 | Off to a cold start: New York University's 2013 knowledge base population systems | 3 |
| 6 | Distant Supervision for Relation Extraction with an Incomplete Knowledge Base | 135 |
| 7 | Towards Fine-grained Citation Function Classification | 21 |
| 8 | Gathering and Generating Paraphrases from Twitter with Application to Normalization | 24 |
| 9 | Confidence Estimation for Knowledge Base Population | 5 |
| 10 | Paraphrasing for Style | 49 |
| 11 | Can Document Selection Help Semi-supervised Learning? A Case Study On Event Extraction | 10 |
| 12 | Semi-supervised Relation Extraction with Large-scale Word Clustering | 75 |
| 13 | The impact of task and corpus on event extraction systems | 5 |
| 14 | Annotating Noun Argument Structure for NomBank | 72 |
| 15 | 18 | |
| 16 | Towards Best Practice for Multiword Expressions in Computational Lexicons | 82 |
| 17 | From Resources to Applications. Designing the Multilingual ISLE Lexical Entry. | 6 |
| 18 | The American National Corpus: A standardized resource for American English | 34 |
| 19 | A treebank of Spanish and its application to parsing | 17 |
| 20 | A production rule system for message summarization | 5 |
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.