Ralph Grishman

About

Ralph Grishman is a professor in the Department of Computer Science at New York University, where he has served since 1983 and was chairman from 1986 to 1988. His academic background includes a Ph.D. in Physics from Columbia University, earned in 1973, with a thesis on Numerical Studies of Self-Avoiding Walks, and an A.B. in Physics from Columbia College, graduated summa cum laude in 1968. His early career involved roles such as Assistant Professor, Associate Professor, and Associate Research Scientist at the Courant Institute of Mathematical Sciences, NYU, and he has also held positions at Barnard College and Columbia University, among others. His professional service includes leadership roles in the Association for Computational Linguistics, where he served as Vice President and President, and participation in government committees such as ARPA and NIST, focusing on speech, natural language processing, and text analysis. His research contributions are centered on natural language processing, computational linguistics, sublanguage processing, and language analysis, with a significant focus on developing computational models for language understanding, information extraction, and domain-specific language analysis.

Research topics

• Artificial Intelligence
• Machine Learning
• Computer Science
• Data Mining
• Natural Language Processing
• Information Retrieval
• Mathematics
• Programming language

Selected publications

• Joint Event Extraction via Recurrent Neural Networks

  2016-01-01 · 661 citations

  articleOpen accessSenior author

  Event extraction is a particularly challenging problem in information extraction. The stateof-the-art models for this problem have either applied convolutional neural networks in a pipelined framework The former is able to learn hidden feature representations automatically from data based on the continuous and generalized representations of words. The latter, on the other hand, is capable of mitigating the error propagation problem of the pipelined approach and exploiting the inter-dependencies…

  PublisherOA PDFDOI

• Graph Convolutional Networks With Argument-Aware Pooling for Event Detection

  Proceedings of the AAAI Conference on Artificial Intelligence · 2018-04-26 · 382 citations

  articleOpen accessSenior author

  The current neural network models for event detection have only considered the sequential representation of sentences. Syntactic representations have not been explored in this area although they provide an effective mechanism to directly link words to their informative context for event detection in the sentences. In this work, we investigate a convolutional neural network based on dependency trees to perform event detection. We propose a novel pooling method that relies on entity mentions to ag…

  PublisherOA PDFDOI

• Modeling Skip-Grams for Event Detection with Convolutional Neural Networks

  2016-01-01 · 120 citations

  articleOpen accessSenior authorCorresponding

  Convolutional neural networks (CNN) have achieved the top performance for event detection due to their capacity to induce the underlying structures of the k-grams in the sentences. However, the current CNN-based event detectors only model the consecutive k-grams and ignore the non-consecutive kgrams that might involve important structures for event detection. In this work, we propose to improve the current CNN models for ED by introducing the non-consecutive convolution. Our systematic evaluatio…

  PublisherOA PDFDOI

• Lexicalized Dependency Paths Based Supervised Learning for Relation Extraction

  Computer Systems Science and Engineering · 2022 · 85 citations

  Senior authorCorresponding

  Log-linear models and more recently neural network models used for supervised relation extraction requires substantial amounts of training data and time, limiting the portability to new relations and domains. To this end, we propose a training representation based on the dependency paths between entities in a dependency tree which we call lexicalized dependency paths (LDPs). We show that this representation is fast, efficient and transparent. We further propose representations utilizing entity t…

  PublisherOA PDFDOI

• Combining Neural Networks and Log-linear Models to Improve Relation Extraction

  arXiv (Cornell University) · 2015-11-18 · 82 citations

  preprintOpen accessSenior author

  The last decade has witnessed the success of the traditional feature-based method on exploiting the discrete structures such as words or lexical patterns to extract relations from text. Recently, convolutional and recurrent neural networks has provided very effective mechanisms to capture the hidden structures within sentences via continuous representations, thereby significantly advancing the performance of relation extraction. The advantage of convolutional neural networks is their capacity to…

  PublisherOA PDFDOI

Frequent coauthors

• Adam Meyers

  40 shared

• Catherine Macleod

  Bangor University

  35 shared

• Heng Ji

  25 shared

• Roman Yangarber

  24 shared

• John Sterling

  21 shared

• Satoshi Sekine

  18 shared

• Lynette Hirschman

  Mitre (United States)

  17 shared

• Bonan Min

  16 shared

Labs

• NYU Courant Institute of Mathematical SciencesPI

Similar researchers at New York University

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• Dennis Shashah-84
• Mehryar Mohrih-77