Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation

Zhang, Zixuan; Parulian, Nikolaus Nova; Ji, Heng; El-Sayed, Ahmed; Myers, Skatje; Palmer, Martha

doi:10.18653/v1/2021.acl-long.489

Cited by 13 publications

(5 citation statements)

References 18 publications

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“…Although the biomedical domain has been more popular than chemistry (Zheng et al, 2014;Islamaj Dogan et al, 2019;Zhang et al, 2021;Lai et al, 2021), information retrieval in chemistry has long been studied and is summarized by Krallinger et al (2017). Most work has focused on only a single modality: text or molecules.…”

Section: Substructure or Description Retrievalmentioning

confidence: 99%

Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries

Edwards¹,

Zhai²,

Ji³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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We propose a new task, Text2Mol, to retrieve molecules using natural language descriptions as queries. Natural language and molecules encode information in very different ways, which leads to the exciting but challenging problem of integrating these two very different modalities. Although some work has been done on text-based retrieval and structurebased retrieval, this new task requires integrating molecules and natural language more directly. Moreover, this can be viewed as an especially challenging cross-lingual retrieval problem by considering the molecules as a language with a very unique grammar. We construct a paired dataset of molecules and their corresponding text descriptions, which we use to learn an aligned common semantic embedding space for retrieval. We extend this to create a cross-modal attention-based model for explainability and reranking by interpreting the attentions as association rules. We also employ an ensemble approach to integrate our different architectures, which significantly improves results from 0.372 to 0.499 MRR. This new multimodal approach opens a new perspective on solving problems in chemistry literature understanding and molecular machine learning. 1 1 The programs and data are publicly available at github.com/cnedwards/text2mol for research purposes.Water is an oxygen hydride consisting of an oxygen atom that is covalently bonded to two hydrogen atoms.

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Section: Substructure or Description Retrievalmentioning

confidence: 99%

Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries

Edwards¹,

Zhai²,

Ji³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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“…IE is a key component in supporting knowledge acquisition and it impacts a wide spectrum of knowledge-driven AI applications. We will conclude the tutorial by presenting further challenges and potential research topics in identifying trustworthiness of extracted content (Zhang et al, , 2020b, IE with quantitative reasoning (Elazar et al, 2019;, cross-document IE (Caciularu et al, 2021), incorporating domainspecific knowledge Zhang et al, 2021c), extension to knowledge reasoning and prediction, modeling of label semantics Mueller et al, 2022;Ma et al, 2022;Chen et al, 2020a), and challenges for acquiring implicit but essential information from corpora that potentially involve reporting bias (Sap et al, 2020).…”

Section: Future Research Directions [30min]mentioning

confidence: 99%

New Frontiers of Information Extraction

Chen¹,

Huang²,

Li³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources. In particular, this tutorial will provide audience with a systematic introduction to recent advances in IE, by addressing several important research questions. These questions include (i) how to develop a robust IE system from a small amount of noisy training data, while ensuring the reliability of its prediction? (ii) how to foster the generalizability of IE through enhancing the system's cross-lingual, crossdomain, cross-task and cross-modal transferability? (iii) how to support extracting structural information with extremely fine-grained and diverse labels? (iv) how to further improve IE by leveraging indirect supervision from other NLP tasks, such as Natural Language Generation (NLG), Natural Language Inference (NLI), Question Answering (QA) or summarization, and pre-trained language models? (v) how to acquire knowledge to guide inference in IE systems? We will discuss several lines of frontier research that tackle those challenges, and will conclude the tutorial by outlining directions for further investigation.

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“…There have been recent studies on improving information extraction with external data [11], [33]- [35], to help enrich or disambiguate local information. In [33], the authors develop a model that aligns nodes in span graph and knowledge graph to learn a more distinctive concept embedding for joint biomedical entity and relation extraction.…”

Section: E Improve Information Extraction With External Knowledgementioning

confidence: 99%

Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation

Sun¹,

Li²,

Xiao³

et al. 2021

Preprint

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Automated knowledge discovery from trending chemical literature is essential for more efficient biomedical research. How to extract detailed knowledge about chemical reactions from the core chemistry literature is a new emerging challenge that has not been well studied. In this paper, we study the new problem of fine-grained chemical entity typing, which poses interesting new challenges especially because of the complex name mentions frequently occurring in chemistry literature and graphic representation of entities. We introduce a new benchmark data set (CHEMET) to facilitate the study of the new task and propose a novel multi-modal representation learning framework to solve the problem of fine-grained chemical entity typing by leveraging external resources with chemical structures and using cross-modal attention to learn effective representation of text in the chemistry domain. Experiment results show that the proposed framework outperforms multiple state-of-the-art methods. 1

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Fine-grained Information Extraction from Biomedical Literature based on Knowledge-enriched Abstract Meaning Representation

Cited by 13 publications

References 18 publications

Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries

Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries

New Frontiers of Information Extraction

Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation

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