45 citations · 57 across the 2 of their papers we have counts for
7 papers
Knowledge Enhanced Contextual Word Representations
Matthew E. Peters, Mark Neumann, Robert L. Logan +4
Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember fa…
Grammar-based Neural Text-to-SQL Generation
Kevin Lin, Ben Bogin, Mark Neumann +2
The sequence-to-sequence paradigm employed by neural text-to-SQL models typically performs token-level decoding and does not consider generating SQL hierarchically from a grammar.…
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing
Mark Neumann, Daniel King, Iz Beltagy +1
Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical…
Dissecting Contextual Word Embeddings: Architecture and Representation
Matthew E. Peters, Mark Neumann, Luke Zettlemoyer +1
Contextual word representations derived from pre-trained bidirectional language models (biLMs) have recently been shown to provide significant improvements to the state of the art…
Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context
Lucy Lu Wang, Chandra Bhagavatula, Mark Neumann +3
Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, re…
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer +4
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses var…