A study on developing novel methods for relation extraction

Relation Extraction (RE) is a task of Natural Language Processing (NLP) to detect and classify the relations between two entities. Relation extraction in the biomedical and scientific literature domain is challenging as text can contain multiple pairs of entities in the same instance. During the course of this research, we developed an RE framework (RelEx), which consists of five main RE paradigms: rule-based, machine learning-based, Convolutional Neural Network (CNN)-based, Bidirectional Encoder Representations from Transformers (BERT)-based, and Graph Convolutional Networks (GCNs)-based approaches. RelEx\u27s rule-based approach uses co-location information of the entities to determine whether a relation exists between a selected entity and the other entities. RelEx\u27s machine learning-based approach consists of traditional feature representations into traditional machine learning algorithms. RelEx\u27s CNN-based approach consists of three CNN architectures: Segment-CNN, single-label Sentence-CNN, and multi-label Sentence-CNN. RelEx\u27s BERT-based approach utilizes BERT\u27s contextualized word embeddings into a feed-forward neural network. Finally, RelEx\u27s GCN-based approach consists of two GCN-based architectures: GCN-Vanilla, GCN-BERT. We evaluated variations of these approaches in two different domains across four distinct relation types. Overall our findings showed that the rule-based approach is applicable for data with fewer instances in the training data. In contrast, the CNN-based, BERT-based, and GCN-based approaches perform better with labeled data with many training instances. These approaches automatically identify patterns in the data efficiently, whereas rule-based approaches require expert knowledge to generate rules. The CNN-based, BERT-based approaches capture the local contextual information within a sentence or document by embedding both semantic and syntactic information in a learned representation. However, their ability to capture the long-range dependency global information in a text is limited. GCN-based approaches capture the global association information by performing convolution operations on neighbor nodes in a graph and incorporating information from neighbors. Combining GCN with BERT integrates the local contextual and global association information of the words and generates better representations for the words

An Extended Overview of the CLEF 2020 ChEMU Lab: Information Extraction of Chemical Reactions from Patents

The discovery of new chemical compounds is perceived as a key driver of the chemistry industry and many other economic sectors. The information about the new discoveries are usually disclosed in scientific literature and in particular, in chemical patents, since patents are often the first venues where the new chemical compounds are publicized. Despite the significance of the information provided in chemical patents, extracting the information from patents is costly due to the large volume of existing patents and its drastic expansion rate. The Cheminformatics Elsevier Melbourne University (ChEMU) evaluation lab 2020, part of the Conference and Labs of the Evaluation Forum 2020 (CLEF2020), provides a platform to advance the state-of-the-arts in automatic information extraction systems over chemical patents. In particular, we focus on extracting synthesis process of new chemical compounds from chemical patents. Using the ChEMU corpus of 1500 “snippets” (text segments) sampled from 170 patent documents and annotated by chemical experts, we defined two key information extraction tasks. Task 1 targets at chemical named entity recognition, i.e., the identification of chemical compounds and their specific roles in chemical reactions. Task 2 targets at event extraction, i.e., the identification of reaction steps, relating the chemical compounds involved in a chemical reaction. In this paper, we provide an overview of our ChEMU2020 lab. Herein, we describe the resources created for the two tasks, the evaluation methodology adopted, and participants results. We also provide a brief summary of the methods employed by participants of this lab and the results obtained across 46 runs from 11 teams, finding that several submissions achieve substantially better results than the baseline methods prepared by the organizers