‘ C hem X tract’ A System for Extraction of Chemical Events from Patent Documents (2023)

Abstract: ChemXtraxt main goal is to extract the chemical events from patent documents. Event extraction requires that we first identify the names of chemical compounds involved in the events. Thus, in this work two extractions are done and they are (a) names of chemical compounds and (b) event that identify the specific involvement of the chemical compounds in a chemical reaction. Extraction of essential elements of a chemical reaction, generally known as Named Entity Recognition (NER), extracts the compounds, condition and yields, their specific role in reaction and assigns a label according to the role it plays within a chemical reaction. Whereas event extraction identifies the chemical event relations between the chemical compounds identified. Here in this work we have used Neural Conditional Random Fields (NCRF), which combines the power of artificial neural network (ANN) and CRFs. Different levels of features that include linguistic, orthographical and lexical clues are used. The results obtained are encouraging.

## introduction chemical information extraction is a challenging task. unstructured data in the biomedical domain contain descriptions of chemical entities and the extracting these entities from textual data repositories, in particular from the patents, is becoming increasingly important for researchers and for the industry. human annotation of patents to generate annotated corpus and populate chemical databases is a tedious task and this can be made easy and fast through the use of automated language processing. the process of automatically extracting the mentions of a particular semantic type in text is known as information extraction (ie). ie includes the extraction of names of chemical compounds and assigns a label according to the role it plays within the chemical reaction, popularly known as named entity recognition (ner) and also event relation extraction, where it extracts the chemical event relation that takes place between the chemical compounds. chemxtract extracts the chemical compound names and its event relation in patent documents. in this paper we discuss in detail the methods and techniques used in chemxtract. the extraction identify and label chemical compounds and their specific types, i.e. to assign the label of a chemical compound according to the role which it plays within a chemical reaction, the temperature and reaction time at which the chemical reaction is carried out, the yields obtained for the final chemical product and the label of the reaction. the challenges in extracting the chemical compounds are many and it further increases when it is from patent documents. the language used in patents is very different from the language used in scientific literature. when writing scientific papers, authors strive to make their words as clear and straightforward as possible, whereas patent authors often seek to protect their knowledge from being fully disclosed @xcite . thus the main challenges for natural language processing (nlp) in patent documents arise from its writing style such as long and complex sentences and long list of chemical compounds. as the characteristics of sentences in patent documents bring in challenges in deep syntactic parsing, in this work we have used shallow parsing of the documents. the data used for this work is provided by chemu, clef 2020 @xcite . the features and factors used include linguistic, orthographical and lexical clues. further the paper is structured as follows, in section 2, a brief overview of the recent published work is given and section 3 details the features and the methods used in the development of the named entity recognizer. the section 4 describes the event extraction, and the evaluation and results are discussed in section 5. the paper ends with the conclusion ## literature review in recent years deep learning is flourishing as a well-known ml methodology for nlp applications. by using the multilayer neural architecture it can learn the hidden patterns from the enormous amount of data and handles the complex problems. in chemical informatics which is a sub-field of bionlp the use of deep learning for various application related to extraction of information is flourishing as seen in bioie. biomedical information extraction (bioie) automatically extracts relevant structured semantics (e.g. entities, relations and events) from unstructured biomedical text data. bioie covers a large spectrum of research efforts which includes the tasks such as named entity recognition @xcite @xcite , event identification @xcite @xcite , and relation extraction @xcite @xcite . the domains include medical literature @xcite , biological literature @xcite , electronic health records @xcite , and chemical name extraction @xcite . the methodology includes rulebased, knowledge-based, statistics based, learning-based methods and hybrid methods @xcite . the extraction of information, which uses the natural language processing (nlp) techniques to extract relevant information to understand the underlying mechanisms of disease, is summarized in gonzalez et al. @xcite . deep learning networks can be roughly categorized into (1) unsupervised/generative, e.g., restricted boltzmann machines (rbms) @xcite , deep belief networks (dbns) @xcite ; learning methods used in bioie falls into three categories: (1) learning from labeled data (i.e. supervised learning); (2) learning from unlabeled data (i.e. semi-supervised and unsupervised learning); (3) hybrid approach where learning scheme integration to integrate different learning paradigms at outer system level. the approaches used in bioie are conditional random fields(crf) @xcite and support vector machines(ssvm) @xcite which are supervised learning methods, and deep neural networks @xcite which is unsupervised approach and these have been applied to both general domain ie and bioie. a scalable and reliable approach on ie is the open information extraction (openie) @xcite , which has emerged as a novel information extraction paradigm. openie systems consist of four main components: @xcite automatic labeling of data using heuristics or distant supervision; (2) extractor learning using relation-independent features on noisy self-labeled data; (3) tuple 990 3 extraction on a large amount of text by the extractor; (4) accuracy assessing by assigning each tuple a probability or confidence score. ## extraction of chemical entity and its event relations chemxtract extracts chemical entities and its event relation. it has two components 1) chemical name identification and 2) event relation identification. the system follows a pipeline architecture, where the data is first pre-processed to the required format that is needed to train the system. after training the system the nes are automatically identified from the test set. the overall system architecture is shown in figure 1 . the following section gives in detail the preprocessing required for both the tasks. ## evaluation, results and discussion we use the standard evaluation metrics of precision, recall and f measure for evaluating chemical compounds and events detection. ## conclusion chemxtract works on extracting names of chemical compounds and event that identify the specific involvement of the chemical compounds in a chemical reaction. we have used neural conditional random fields (ncrfs) to identify and extract chemical compounds. the patent documents were preprocessed using nlp tools for obtaining syntactic information, part-of-speech and noun/verb phrases. the relationships between the chemical compounds are based on the chemical reaction events. again the same neural conditional random fields (ncrfs) is used to identify the relationships and the relation arguments. the results obtained are encouraging and comparable with the state of the art.