Waleed Ammar


2020

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SUPP.AI: finding evidence for supplement-drug interactions
Lucy Wang | Oyvind Tafjord | Arman Cohan | Sarthak Jain | Sam Skjonsberg | Carissa Schoenick | Nick Botner | Waleed Ammar
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Dietary supplements are used by a large portion of the population, but information on their pharmacologic interactions is incomplete. To address this challenge, we present SUPP.AI, an application for browsing evidence of supplement-drug interactions (SDIs) extracted from the biomedical literature. We train a model to automatically extract supplement information and identify such interactions from the scientific literature. To address the lack of labeled data for SDI identification, we use labels of the closely related task of identifying drug-drug interactions (DDIs) for supervision. 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. We extract 195k evidence sentences from 22M articles (P=0.82, R=0.58, F1=0.68) for 60k interactions. We create the SUPP.AI application for users to search evidence sentences extracted by our model. SUPP.AI is an attempt to close the information gap on dietary supplements by making up-to-date evidence on SDIs more discoverable for researchers, clinicians, and consumers. An informational video on how to use SUPP.AI is available at: https://youtu.be/dR0ucKdORwc

2019

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Combining Distant and Direct Supervision for Neural Relation Extraction
Iz Beltagy | Kyle Lo | Waleed Ammar
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

In relation extraction with distant supervision, noisy labels make it difficult to train quality models. Previous neural models addressed this problem using an attention mechanism that attends to sentences that are likely to express the relations. We improve such models by combining the distant supervision data with an additional directly-supervised data, which we use as supervision for the attention weights. We find that joint training on both types of supervision leads to a better model because it improves the model’s ability to identify noisy sentences. In addition, we find that sigmoidal attention weights with max pooling achieves better performance over the commonly used weighted average attention in this setup. Our proposed method achieves a new state-of-the-art result on the widely used FB-NYT dataset.

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Structural Scaffolds for Citation Intent Classification in Scientific Publications
Arman Cohan | Waleed Ammar | Madeleine van Zuylen | Field Cady
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

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. We propose structural scaffolds, a multitask model to incorporate structural information of scientific papers into citations for effective classification of citation intents. Our model achieves a new state-of-the-art on an existing ACL anthology dataset (ACL-ARC) with a 13.3% absolute increase in F1 score, without relying on external linguistic resources or hand-engineered features as done in existing methods. In addition, we introduce a new dataset of citation intents (SciCite) which is more than five times larger and covers multiple scientific domains compared with existing datasets. Our code and data are available at: https://github.com/allenai/scicite.

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Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)
Waleed Ammar | Annie Louis | Nasrin Mostafazadeh
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

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ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing
Mark Neumann | Daniel King | Iz Beltagy | Waleed Ammar
Proceedings of the 18th BioNLP Workshop and Shared Task

Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scispaCy, a new Python library and models for practical biomedical/scientific text processing, which heavily leverages the spaCy library. We detail the performance of two packages of models released in scispaCy and demonstrate their robustness on several tasks and datasets. Models and code are available at https://allenai.github.io/scispacy/.

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GrapAL: Connecting the Dots in Scientific Literature
Christine Betts | Joanna Power | Waleed Ammar
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We introduce GrapAL (Graph database of Academic Literature), a versatile tool for exploring and investigating a knowledge base of scientific literature that was semi-automatically constructed using NLP methods. GrapAL fills many informational needs expressed by researchers. At the core of GrapAL is a Neo4j graph database with an intuitive schema and a simple query language. In this paper, we describe the basic elements of GrapAL, how to use it, and several use cases such as finding experts on a given topic for peer reviewing, discovering indirect connections between biomedical entities, and computing citation-based metrics. We open source the demo code to help other researchers develop applications that build on GrapAL.

2018

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Content-Based Citation Recommendation
Chandra Bhagavatula | Sergey Feldman | Russell Power | Waleed Ammar
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We present a content-based method for recommending citations in an academic paper draft. We embed a given query document into a vector space, then use its nearest neighbors as candidates, and rerank the candidates using a discriminative model trained to distinguish between observed and unobserved citations. Unlike previous work, our method does not require metadata such as author names which can be missing, e.g., during the peer review process. Without using metadata, our method outperforms the best reported results on PubMed and DBLP datasets with relative improvements of over 18% in F1@20 and over 22% in MRR. We show empirically that, although adding metadata improves the performance on standard metrics, it favors self-citations which are less useful in a citation recommendation setup. We release an online portal for citation recommendation based on our method, (URL: http://bit.ly/citeDemo) and a new dataset OpenCorpus of 7 million research articles to facilitate future research on this task.

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A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications
Dongyeop Kang | Waleed Ammar | Bhavana Dalvi | Madeleine van Zuylen | Sebastian Kohlmeier | Eduard Hovy | Roy Schwartz
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research purposes (PeerRead v1),1 providing an opportunity to study this important artifact. The dataset consists of 14.7K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR. The dataset also includes 10.7K textual peer reviews written by experts for a subset of the papers. We describe the data collection process and report interesting observed phenomena in the peer reviews. We also propose two novel NLP tasks based on this dataset and provide simple baseline models. 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. In the second task, we predict the numerical scores of review aspects and show that simple models can outperform the mean baseline for aspects with high variance such as ‘originality’ and ‘impact’.

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Construction of the Literature Graph in Semantic Scholar
Waleed Ammar | Dirk Groeneveld | Chandra Bhagavatula | Iz Beltagy | Miles Crawford | Doug Downey | Jason Dunkelberger | Ahmed Elgohary | Sergey Feldman | Vu Ha | Rodney Kinney | Sebastian Kohlmeier | Kyle Lo | Tyler Murray | Hsu-Han Ooi | Matthew Peters | Joanna Power | Sam Skjonsberg | Lucy Wang | Chris Wilhelm | Zheng Yuan | Madeleine van Zuylen | Oren Etzioni
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. The resulting literature graph consists of more than 280M nodes, representing papers, authors, entities and various interactions between them (e.g., authorships, citations, entity mentions). We reduce literature graph construction into familiar NLP tasks (e.g., entity extraction and linking), point out research challenges due to differences from standard formulations of these tasks, and report empirical results for each task. The methods described in this paper are used to enable semantic features in www.semanticscholar.org.

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Ontology alignment in the biomedical domain using entity definitions and context
Lucy Wang | Chandra Bhagavatula | Mark Neumann | Kyle Lo | Chris Wilhelm | Waleed Ammar
Proceedings of the BioNLP 2018 workshop

Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose a method for enriching entities in an ontology with external definition and context information, and use this additional information for ontology alignment. We develop a neural architecture capable of encoding the additional information when available, and show that the addition of external data results in an F1-score of 0.69 on the Ontology Alignment Evaluation Initiative (OAEI) largebio SNOMED-NCI subtask, comparable with the entity-level matchers in a SOTA system.

2017

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The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction
Waleed Ammar | Matthew E. Peters | Chandra Bhagavatula | Russell Power
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our submission for the ScienceIE shared task (SemEval- 2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via neural language models, character-level encoding, gazetteers extracted from existing knowledge bases, and model ensembles. Our official submission ranked first in end-to-end entity and relation extraction (scenario 1), and second in the relation-only extraction (scenario 3).

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Semi-supervised sequence tagging with bidirectional language models
Matthew E. Peters | Waleed Ammar | Chandra Bhagavatula | Russell Power
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks. However, in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data. In this paper, we demonstrate a general semi-supervised approach for adding pretrained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks. We evaluate our model on two standard datasets for named entity recognition (NER) and chunking, and in both cases achieve state of the art results, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers.

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Ontology-Aware Token Embeddings for Prepositional Phrase Attachment
Pradeep Dasigi | Waleed Ammar | Chris Dyer | Eduard Hovy
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase (PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4% absolute points, which amounts to a 34.4% relative reduction in errors.

2016

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Many Languages, One Parser
Waleed Ammar | George Mulcaire | Miguel Ballesteros | Chris Dyer | Noah A. Smith
Transactions of the Association for Computational Linguistics, Volume 4

We train one multilingual model for dependency parsing and use it to parse sentences in several languages. The parsing model uses (i) multilingual word clusters and embeddings; (ii) token-level language information; and (iii) language-specific features (fine-grained POS tags). This input representation enables the parser not only to parse effectively in multiple languages, but also to generalize across languages based on linguistic universals and typological similarities, making it more effective to learn from limited annotations. Our parser’s performance compares favorably to strong baselines in a range of data scenarios, including when the target language has a large treebank, a small treebank, or no treebank for training.

2015

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Model Selection for Type-Supervised Learning with Application to POS Tagging
Kristina Toutanova | Waleed Ammar | Pallavi Choudhury | Hoifung Poon
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

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Constraint-Based Models of Lexical Borrowing
Yulia Tsvetkov | Waleed Ammar | Chris Dyer
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Unsupervised POS Induction with Word Embeddings
Chu-Cheng Lin | Waleed Ammar | Chris Dyer | Lori Levin
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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The CMU Machine Translation Systems at WMT 2014
Austin Matthews | Waleed Ammar | Archna Bhatia | Weston Feely | Greg Hanneman | Eva Schlinger | Swabha Swayamdipta | Yulia Tsvetkov | Alon Lavie | Chris Dyer
Proceedings of the Ninth Workshop on Statistical Machine Translation

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The CMU Submission for the Shared Task on Language Identification in Code-Switched Data
Chu-Cheng Lin | Waleed Ammar | Lori Levin | Chris Dyer
Proceedings of the First Workshop on Computational Approaches to Code Switching

2013

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The CMU Machine Translation Systems at WMT 2013: Syntax, Synthetic Translation Options, and Pseudo-References
Waleed Ammar | Victor Chahuneau | Michael Denkowski | Greg Hanneman | Wang Ling | Austin Matthews | Kenton Murray | Nicola Segall | Alon Lavie | Chris Dyer
Proceedings of the Eighth Workshop on Statistical Machine Translation

2012

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Transliteration by Sequence Labeling with Lattice Encodings and Reranking
Waleed Ammar | Chris Dyer | Noah Smith
Proceedings of the 4th Named Entity Workshop (NEWS) 2012

2011

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Improved Transliteration Mining Using Graph Reinforcement
Ali El Kahki | Kareem Darwish | Ahmed Saad El Din | Mohamed Abd El-Wahab | Ahmed Hefny | Waleed Ammar
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing