ChemicalTagger: A tool for semantic text-mining in chemistry

Abstract

BackgroundThe primary method for scientific communication is in the form of published scientific articles and theses which use natural language combined with domain-specific terminology. As such, they contain free owing unstructured text. Given the usefulness of data extraction from unstructured literature, we aim to show how this can be achieved for the discipline of chemistry. The highly formulaic style of writing most chemists adopt make their contributions well suited to high-throughput Natural Language Processing (NLP) approaches.ResultsWe have developed the ChemicalTagger parser as a medium-depth, phrase-based semantic NLP tool for the language of chemical experiments. Tagging is based on a modular architecture and uses a combination of OSCAR, domain-specific regex and English taggers to identify parts-of-speech. The ANTLR grammar is used to structure this into tree-based phrases. Using a metric that allows for overlapping annotations, we achieved machine-annotator agreements of 88.9% for phrase recognition and 91.9% for phrase-type identification (Action names).ConclusionsIt is possible parse to chemical experimental text using rule-based techniques in conjunction with a formal grammar parser. ChemicalTagger has been deployed for over 10,000 patents and has identified solvents from their linguistic context with >99.5% precision.

ChemEx: information extraction system for chemical data curation

“…ChemEx employs ChemicalTagger [24], which uses machine learning approach called Maximum Entropy Markov Model Recogniser [25], to (i) recognize chemical names, reaction names, enzymes, and chemistry-related terms such as experimental action verbs or units and (ii) tag general English word classes, such as a noun or a verb, which will be used in the phase parser. ChemEx uses all information from ChemicalTagger.…”

“…ChemicalTagger [24] also parses and identifies a sentence. Phase parser receives tagged token stream and builds grammatical structure based on predefined grammars.…”

Practical Chemoinformatics

“…Return hits only if named entity is resolved to a structure A combination of rule-based chemical text and formal grammar parser has been developed known as ChemicalTAgger [91]. It is a freely available open-source Java-based software which uses both OSCAR and open NLP programs (Fig.…”

“…Databases usually contain many tables. All the tables can be linked by a common identifier such as a primary key within the database or through foreign key association [91]. 1.22 Databases Some of the most familiar terms used in databases are:…”

Virtual screening of inorganic materials synthesis parameters with deep learning

“…[15][16][17][18][19][20][21][22] There is, consequently, a pressing need for analogous virtual screening of inorganic materials syntheses to complement the growing volume of predicted and screened compounds. 23,24 Such synthesis screening approaches have indeed found recent success in organic chemistry, where a wealth of tabulated reaction data is available, [25][26][27][28][29][30][31][32][33][34][35] and synthesis parameter screening, driven by machine learning, has also been explored for the specific case of organically templated metal vanadium selenites. 20 These efforts have laid the groundwork for analogous large-scale inorganic synthesis screening.…”