PAWLS: PDF Annotation With Labels and Structure

1 code implementation • ACL 2021 • 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.

PySBD: Pragmatic Sentence Boundary Disambiguation

1 code implementation • EMNLP (NLPOSS) 2020 • 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.

Sentence

S2ORC: The Semantic Scholar Open Research Corpus

2 code implementations • ACL 2020 • Kyle Lo, Lucy Lu Wang, Mark Neumann, Rodney Kinney, Dan S. Weld

We introduce S2ORC, a large corpus of 81. 1M English-language academic papers spanning many academic disciplines.

Language Modelling

Knowledge Enhanced Contextual Word Representations

1 code implementation • IJCNLP 2019 • Matthew E. Peters, Mark Neumann, Robert L. Logan IV, Roy Schwartz, Vidur Joshi, Sameer Singh, Noah A. Smith

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 facts about those entities.

Entity Linking Entity Typing +3

Grammar-based Neural Text-to-SQL Generation

no code implementations • 30 May 2019 • Kevin Lin, Ben Bogin, Mark Neumann, Jonathan Berant, Matt Gardner

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.

Semantic Parsing Text-To-SQL

ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing

1 code implementation • WS 2019 • Mark Neumann, Daniel King, Iz Beltagy, Waleed Ammar

Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift.

Dissecting Contextual Word Embeddings: Architecture and Representation

no code implementations • EMNLP 2018 • Matthew E. Peters, Mark Neumann, Luke Zettlemoyer, Wen-tau Yih

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 for a wide range of NLP tasks.

Word Embeddings

Ontology Alignment in the Biomedical Domain Using Entity Definitions and Context

1 code implementation • WS 2018 • Lucy Lu Wang, Chandra Bhagavatula, Mark Neumann, Kyle Lo, Chris Wilhelm, Waleed Ammar

Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies.

AllenNLP: A Deep Semantic Natural Language Processing Platform

1 code implementation • WS 2018 • Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson Liu, Matthew Peters, Michael Schmitz, Luke Zettlemoyer

This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding.

Natural Language Understanding Reading Comprehension +1

Deep contextualized word representations

46 code implementations • NAACL 2018 • Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer

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 vary across linguistic contexts (i. e., to model polysemy).

Citation Intent Classification Conversational Response Selection +8

Learning to Reason With Adaptive Computation

no code implementations • 24 Oct 2016 • 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.

BIG-bench Machine Learning Natural Language Inference +1