Chemical substances or entities are important terms in chemistry publications and patents. Various representations are available to represent chemical entities like IUPAC, trivial names, SMILES, InChI and CAS Registry numbers. Chemical names pose a special challenge in information retrieval since they typically are long and complex expressions and prone to variation, which in turn may cause a decrease in retrieval performance.
The difficulty in obtaining manually annotated data for training NER systems has motivated researchers to look for alternative ways of generating annotated data, or for making the best possible use of unlabeled data. Several systems address the problem regarding chemical entities with a variety of approaches. In this paper, we present a Pattern Matching approach to find the IUPAC names in chemical documents.
Alexander Vasserman [11] (2004) identify chemical names in Biomedical Text using substring co-occurrence based approaches. In this work, models were built based on the difference between strings occurring in chemical names and strings that occur in other words. The models are trained from a dictionary of chemical names and general biomedical text. A new way of interpolating N-grams was introduced that does not require tuning any parameters.
Zornitsa Kozareva [5] (2006) proposed and implemented a pattern validation search in an unlabeled corpus through which gazetteer lists were automatically generated. The gazetteers were used as features by a Named Entity Recognition system. A comparative study of information contributed by the gazetteers in the entity classification process was shown. Andreas Vlachos et al. [3] (2006) demonstrated empirically the efficiency of using automatically created tra...
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Tim Rocktäschel, Michael Weidlich and Ulf Leser, 2012, [2] presented a named entity recognition tool for identifying mentions of chemicals in natural language texts, including trivial names, drugs, abbreviations, molecular formulas and IUPAC entities. They used a hybrid approach combining a Conditional Random Field with a dictionary. It achieves an F1 measure of 68.1% on the SCAI corpus, outperforming the OSCAR 4 chemical NER tool.
A common problem in chemical NER is the sparsity of annotated corpora for training. In this work, we use the chemical research articles of Indian Journal of Chemistry (Section B) for the extraction of chemical terms using pattern matching and the extracted entities are evaluated using ChEBI dictionary of molecular entities which uses the nomenclature of International Union of Pure and Applied Chemistry for chemical entities.
Kay Arthur teaches how to recognize key words and phrases by creating lists, summarizing chapt...
We have to emphasize the importance of memorizing certain names and formulas and some prefixes and suffixes that are used in building a system of nomenclature. From there on, it is a matter of applying the system to different names and formulas you meet. The summary all the ideas that will be presented in this essay help you to learn the nomenclature system.
ingredient was put on labels. In fact, this act was given to the Bureau of Chemistry section in the
The term “Controlled Vocabulary” is not universally understood by all to mean the same thing. So that it can be used freely without misunderstanding, this paper defines the term as a “considered list of values, designed to improve searchability”. A set of “rules of thumb” are provided for use in the determination of whether a given set of values is a Controlled Vocabulary, and guidance is provided on populating one.
While the Dewey decimal system contains a comprehensive index, the Library of Congress Classification system does not (Taylor 430). Each volume of the LCC schedules contains its own index and these indexes do not refer to one another. Finding subjects in the schedules can be awkward. To locate a topic, one must check through each volume index of all the different disciplines that may ...
This paper is intended to be an introductory tutorial on the Very Large Knowledge Base (VLKB) called CYC. Described herein is the reasoning for the origination of the CYC project, the intended usefulness of the project (application areas), how CYC is being constructed, and a brief introduction to the supporting tools that have been developed to interact with the CYC knowledge base.
During this time I learned how to use the computational chemistry programs Spartan and AutoDock Vina in order to perform a similarity analysis on anti-epileptic drugs. The goal of this project was to determine which drug has the best fit based on the calculated binding affinity to the active site on the GABA-A receptor in the human brain. This research is important because it provides useful information to Medicinal Chemists
There are many concepts to be identified with when studying any language. I will explain a few of the ones I see specific to medical terminology in this threaded discussion. Medical terminology is based on anatomy, so an understanding of anatomy will be one of the building blocks to creating a medical terminology vocabulary. Next, medical terminology is made up of compound words. If you have a basic understanding of the prefixes and suffixes you can figure out the basic meaning of the word. Once you have those figured out you can work on the root word. The most important foundational concept is not to assume anything, if in doubt research, if time permits use a medical terminology dictionary, if not ask someone. There are no do overs in real life so err on the side of caution.
VMD can be useful to a range of audience, molecular structural data obtained from VMD can be integrated with bioinformatics, which will then provide useful information to researchers of biological system, not only individuals involved with the field of biological science, but also theoretical and experimental researchers of chemical science can utilize the information to scrutinize the chemical structure of molecules. It can also be used in educational institutions to display molecules in a very descriptive manner to students, to give them a broader idea of the structural function.
A standardize language and framework in healthcare is necessary to communicate efficiently with other organizations around the world. Reference terminologies and coding systems are proper solutions to avoid any miscommunications and to have a standardize classification system. Complex healthcare services such as billing and payments, quality assurance, research and public health reporting that contain health information must be capable of delivering a cost-effective and safer results. This can be obtained by adapting an appropriate up to date medical coding classification system based on the purpose and the service provided by each clinical facility. In order to deliver critical information needs of a healthcare organization, adapting and maintaining
Medical Terminology is considered as the current language of medicine. When learning this matter, individual will discover that most medical words are separated into 3 groups that are words created from term parts, terms that aren’t made from word portions, and terms that are considered eponyms. Words that are typically made from word parts often contain 2-3 sections. A root is the main basis of a word that has the chance to become joined together with any type of prefix or suffix. A prefix is defined as a word that is positioned before the root word to alter that words meaning. A suffix is defined as a word that is positioned after the root word to then again alter and to provide the implication of the root word that is in place. The combing
Medical terminology is mostly formed from Greek and Latin roots. Terms named for people and are called eponyms. Medical terms can be understood by dissecting them. A root word is used in conjunction with a combining vowel, prefix and/or a suffix to form the term. By learning the meanings of the word parts, most medical terms can be deciphered. Acronyms are often used for common medical terms by using the first letters of each word to create a new word or abbreviation. For example, amyotrophic lateral sclerosis, or Lou Gehrig’s disease, is much better known by its acronym,
The CYC (enCYClopedic) project is so far the most ambitious exploration which requires long- term research and has very high risk in the area of software. The CYC project will completely achieve the goal of a general artificial intelligence and finally change the 21st-centry world radically. This paper intends to introduce the basic knowledge as well as the structure and applications of the CYC project.
The field of clinical medicine, bioinformatics, and research employ an endless list of terms, abbreviations, and codes. To further complicate communication, many of these terms, abbreviations, and codes can vary dramatically due to geography or educational background. This is where the Unified Medical Language System (UMLS) comes into the picture. The UMLS unites nomenclatures such as ICD-9, ICD-10, ICD-10-PCS, CPT, SNOMED, LONIC, and other medical vocabularies into one resource.
Jurafsky, D. & Martin, J. H. (2009), Speech and Language Processing: International Version: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2nd ed, Pearson Education Inc, Upper Saddle River, New Jersey.