Publications (14)
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…
PAWLS: PDF Annotation With Labels and Structure
Mark Neumann, Zejiang Shen, Sam Skjonsberg
Adobe's Portable Document Format (PDF) is a popular way of distributing view-only documents with a rich visual markup. This presents a challenge to NLP practitioners who wish to us…
PySBD: Pragmatic Sentence Boundary Disambiguation
Nipun Sadvilkar, Mark Neumann
In this paper, we present a rule-based sentence boundary disambiguation Python package that works out-of-the-box for 22 languages. We aim to provide a realistic segmenter which can…
S2ORC: The Semantic Scholar Open Research Corpus
Kyle Lo, Lucy Lu Wang, Mark Neumann +2
We introduce S2ORC, a large corpus of 81.1M English-language academic papers spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts, resolved bib…
Orb: A Fast, Scalable Neural Network Potential
Mark Neumann, James Gin, Benjamin Rhodes +5
We introduce Orb, a family of universal interatomic potentials for atomistic modelling of materials. Orb models are 3-6 times faster than existing universal potentials, stable unde…
Mofasa: A Step Change in Metal-Organic Framework Generation
Vaidotas Simkus, Anders Christensen, Steven Bennett +5
Mofasa is an all-atom latent diffusion model with state-of-the-art performance for generating Metal-Organic Frameworks (MOFs). These are highly porous crystalline materials used to…
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…
AllenNLP: A Deep Semantic Natural Language Processing Platform
Matt Gardner, Joel Grus, Mark Neumann +6
This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding. AllenNLP is designed to support researchers who want to build nov…
Learning to Reason With Adaptive Computation
Mark Neumann, Pontus Stenetorp, Sebastian Riedel
Multi-hop inference is necessary for machine learning systems to successfully solve tasks such as Recognising Textual Entailment and Machine Reading. In this work, we demonstrate t…
Orb-v3: atomistic simulation at scale
Benjamin Rhodes, Sander Vandenhaute, Vaidotas Å imkus +4
We introduce Orb-v3, the next generation of the Orb family of universal interatomic potentials. Models in this family expand the performance-speed-memory Pareto frontier, offering…
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.…
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…