Search Results for author: Waleed Ammar

Found 23 papers, 14 papers with code

SUPP.AI: Finding Evidence for Supplement-Drug Interactions

1 code implementation ACL 2020 Lucy Lu Wang, Oyvind Tafjord, Arman Cohan, Sarthak Jain, Sam Skjonsberg, Carissa Schoenick, Nick Botner, Waleed Ammar

We fine-tune the contextualized word representations of the RoBERTa language model using labeled DDI data, and apply the fine-tuned model to identify supplement interactions.

General Classification Language Modelling

Structural Scaffolds for Citation Intent Classification in Scientific Publications

1 code implementation NAACL 2019 Arman Cohan, Waleed Ammar, Madeleine van Zuylen, Field Cady

Identifying the intent of a citation in scientific papers (e. g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature.

 Ranked #1 on Citation Intent Classification on ACL-ARC (using extra training data)

Citation Intent Classification Classification +4

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.

Combining Distant and Direct Supervision for Neural Relation Extraction

1 code implementation NAACL 2019 Iz Beltagy, Kyle Lo, Waleed Ammar

In relation extraction with distant supervision, noisy labels make it difficult to train quality models.

Relation Extraction

A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications

1 code implementation NAACL 2018 Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz

In the first task, we show that simple models can predict whether a paper is accepted with up to 21% error reduction compared to the majority baseline.

Extracting Scientific Figures with Distantly Supervised Neural Networks

1 code implementation6 Apr 2018 Noah Siegel, Nicholas Lourie, Russell Power, Waleed Ammar

Non-textual components such as charts, diagrams and tables provide key information in many scientific documents, but the lack of large labeled datasets has impeded the development of data-driven methods for scientific figure extraction.

Ontology-Aware Token Embeddings for Prepositional Phrase Attachment

1 code implementation ACL 2017 Pradeep Dasigi, Waleed Ammar, Chris Dyer, Eduard Hovy

Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language.

Prepositional Phrase Attachment Word Embeddings

DyNet: The Dynamic Neural Network Toolkit

4 code implementations15 Jan 2017 Graham Neubig, Chris Dyer, Yoav Goldberg, Austin Matthews, Waleed Ammar, Antonios Anastasopoulos, Miguel Ballesteros, David Chiang, Daniel Clothiaux, Trevor Cohn, Kevin Duh, Manaal Faruqui, Cynthia Gan, Dan Garrette, Yangfeng Ji, Lingpeng Kong, Adhiguna Kuncoro, Gaurav Kumar, Chaitanya Malaviya, Paul Michel, Yusuke Oda, Matthew Richardson, Naomi Saphra, Swabha Swayamdipta, Pengcheng Yin

In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives.

graph construction

Massively Multilingual Word Embeddings

1 code implementation5 Feb 2016 Waleed Ammar, George Mulcaire, Yulia Tsvetkov, Guillaume Lample, Chris Dyer, Noah A. Smith

We introduce new methods for estimating and evaluating embeddings of words in more than fifty languages in a single shared embedding space.

Multilingual Word Embeddings Text Categorization

Unsupervised POS Induction with Word Embeddings

no code implementations HLT 2015 Chu-Cheng Lin, Waleed Ammar, Chris Dyer, Lori Levin

Unsupervised word embeddings have been shown to be valuable as features in supervised learning problems; however, their role in unsupervised problems has been less thoroughly explored.

POS Word Embeddings

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