Clayton Morrison


2022

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Low Resource Causal Event Detection from Biomedical Literature
Zhengzhong Liang | Enrique Noriega-Atala | Clayton Morrison | Mihai Surdeanu
Proceedings of the 21st Workshop on Biomedical Language Processing

Recognizing causal precedence relations among the chemical interactions in biomedical literature is crucial to understanding the underlying biological mechanisms. However, detecting such causal relation can be hard because: (1) many times, such causal relations among events are not explicitly expressed by certain phrases but implicitly implied by very diverse expressions in the text, and (2) annotating such causal relation detection datasets requires considerable expert knowledge and effort. In this paper, we propose a strategy to address both challenges by training neural models with in-domain pre-training and knowledge distillation. We show that, by using very limited amount of labeled data, and sufficient amount of unlabeled data, the neural models outperform previous baselines on the causal precedence detection task, and are ten times faster at inference compared to the BERT base model.

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Learning Open Domain Multi-hop Search Using Reinforcement Learning
Enrique Noriega-Atala | Mihai Surdeanu | Clayton Morrison
Proceedings of the Workshop on Structured and Unstructured Knowledge Integration (SUKI)

We propose a method to teach an automated agent to learn how to search for multi-hop paths of relations between entities in an open domain. The method learns a policy for directing existing information retrieval and machine reading resources to focus on relevant regions of a corpus. The approach formulates the learning problem as a Markov decision process with a state representation that encodes the dynamics of the search process and a reward structure that minimizes the number of documents that must be processed while still finding multi-hop paths. We implement the method in an actor-critic reinforcement learning algorithm and evaluate it on a dataset of search problems derived from a subset of English Wikipedia. The algorithm finds a family of policies that succeeds in extracting the desired information while processing fewer documents compared to several baseline heuristic algorithms.

2020

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MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions
Maria Alexeeva | Rebecca Sharp | Marco A. Valenzuela-Escárcega | Jennifer Kadowaki | Adarsh Pyarelal | Clayton Morrison
Proceedings of the Twelfth Language Resources and Evaluation Conference

Extending machine reading approaches to extract mathematical concepts and their descriptions is useful for a variety of tasks, ranging from mathematical information retrieval to increasing accessibility of scientific documents for the visually impaired. This entails segmenting mathematical formulae into identifiers and linking them to their natural language descriptions. We propose a rule-based approach for this task, which extracts LaTeX representations of formula identifiers and links them to their in-text descriptions, given only the original PDF and the location of the formula of interest. We also present a novel evaluation dataset for this task, as well as the tool used to create it.

2019

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Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models
Rebecca Sharp | Adarsh Pyarelal | Benjamin Gyori | Keith Alcock | Egoitz Laparra | Marco A. Valenzuela-Escárcega | Ajay Nagesh | Vikas Yadav | John Bachman | Zheng Tang | Heather Lent | Fan Luo | Mithun Paul | Steven Bethard | Kobus Barnard | Clayton Morrison | Mihai Surdeanu
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed approach is implemented through three systems: Eidos, INDRA, and Delphi. Eidos is an open-domain machine reading system designed to extract causal relations from natural language. It is rule-based, allowing for rapid domain transfer, customizability, and interpretability. INDRA aggregates multiple sources of causal information and performs assembly to create a coherent knowledge base and assess its reliability. This assembled knowledge serves as the starting point for modeling. Delphi is a modeling framework that assembles quantified causal fragments and their contexts into executable probabilistic models that respect the semantics of the original text, and can be used to support decision making.

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Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods
Enrique Noriega-Atala | Zhengzhong Liang | John Bachman | Clayton Morrison | Mihai Surdeanu
Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications

An important task in the machine reading of biochemical events expressed in biomedical texts is correctly reading the polarity, i.e., attributing whether the biochemical event is a promotion or an inhibition. Here we present a novel dataset for studying polarity attribution accuracy. We use this dataset to train and evaluate several deep learning models for polarity identification, and compare these to a linguistically-informed model. The best performing deep learning architecture achieves 0.968 average F1 performance in a five-fold cross-validation study, a considerable improvement over the linguistically informed model average F1 of 0.862.

2018

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WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference
Peter Jansen | Elizabeth Wainwright | Steven Marmorstein | Clayton Morrison
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Learning what to read: Focused machine reading
Enrique Noriega-Atala | Marco A. Valenzuela-Escárcega | Clayton Morrison | Mihai Surdeanu
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Recent efforts in bioinformatics have achieved tremendous progress in the machine reading of biomedical literature, and the assembly of the extracted biochemical interactions into large-scale models such as protein signaling pathways. However, batch machine reading of literature at today’s scale (PubMed alone indexes over 1 million papers per year) is unfeasible due to both cost and processing overhead. In this work, we introduce a focused reading approach to guide the machine reading of biomedical literature towards what literature should be read to answer a biomedical query as efficiently as possible. We introduce a family of algorithms for focused reading, including an intuitive, strong baseline, and a second approach which uses a reinforcement learning (RL) framework that learns when to explore (widen the search) or exploit (narrow it). We demonstrate that the RL approach is capable of answering more queries than the baseline, while being more efficient, i.e., reading fewer documents.