GENERATION OF A LARGE GENE/PROTEIN LEXICON BY MORPHOLOGICAL PATTERN ANALYSIS

2004 ◽  
Vol 01 (04) ◽  
pp. 611-626 ◽  
Author(s):  
LORRAINE TANABE ◽  
W. JOHN WILBUR
Keyword(s):  
Pattern Analysis ◽  
Inductive Logic ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Large Set ◽  
Morphological Pattern ◽  
Natural Language Text ◽  
Named Entity ◽  
Large Gene ◽  
Language Text

The identification of gene/protein names in natural language text is an important problem in named entity recognition. In previous work we have processed MEDLINE© documents to obtain a collection of over two million names of which we estimate that perhaps two thirds are valid gene/protein names. Our problem has been how to purify this set to obtain a high quality subset of gene/protein names. Here we describe an approach which is based on the generation of certain classes of names that are characterized by common morphological features. Within each class inductive logic programming (ILP) is applied to learn the characteristics of those names that are gene/protein names. The criteria learned in this manner are then applied to our large set of names. We generated 193 classes of names and ILP led to criteria defining a select subset of 1,240,462 names. A simple false positive filter was applied to remove 8% of this set leaving 1,145,913 names. Examination of a random sample from this gene/protein name lexicon suggests it is composed of 82% (±3%) complete and accurate gene/protein names, 12% names related to genes/proteins (too generic, a valid name plus additional text, part of a valid name, etc.), and 6% names unrelated to genes/proteins. The lexicon is freely available at .

scholarly journals IMPROVING CHINESE MEDICAL NAMED ENTITY RECOGNITION USING GLYPH AND LEXICON

2021 ◽  
Author(s):  
SHANHAO ZHONG ◽  
QINGSONG YU
Keyword(s):  
Semantic Representation ◽  
Experimental Studies ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entities ◽  
Natural Language Text ◽  
Named Entity ◽  
Structured Information ◽  
Text Information ◽  
Language Text

Abstract. Medical named entity recognition is the first step in processing electronic medical records. It is the basis for processing medical natural language text information into medical structured information, which has extremely high research value and application value. In this paper, we have proposed a model that aims to identify various types of named entities such as disease, imaging examination, laboratory examination, operation, drug, and anatomy from Chinese electronic medical record. We construct a fusion Glyph and lexicon model based on BERT. Experimental studies have shown that increasing character-level semantic representation can improve the performance of named entity recognition. In order to boost it, the major measures of our model include: (1) a CNN structure is proposed to capture glyph information. (2) Soft-Lexicon method is introduced to encode lexicon information. Our models show an improvement over the baseline BERT-BiLSTM-CRF model. The experimental results on CCKS2019 dataset showed that the F1 score was 84.64, which was +1.99 higher than the baseline level.

scholarly journals Joint Posterior Revision of NLP Annotations via Ontological Knowledge

2018 ◽  
Author(s):  
Marco Rospocher ◽  
Francesco Corcoglioniti
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
World Knowledge ◽  
Entity Linking ◽  
Prior Probabilities ◽  
Natural Language Text ◽  
Named Entity ◽  
Ontological Entity ◽  
Language Text ◽  
Common World

Different well-established NLP tasks contribute to elicit the semantics of entities mentioned in natural language text, such as Named Entity Recognition and Classification (NERC) and Entity Linking (EL). However, combining the outcomes of these tasks may result in NLP annotations --- such as a NERC organization linked by EL to a person --- that are unlikely or contradictory when interpreted in the light of common world knowledge about the entities these annotations refer to. We thus propose a general probabilistic model that explicitly captures the relations between multiple NLP annotations for an entity mention, the ontological entity classes implied by those annotations, and the background ontological knowledge those classes may be consistent with. We use the model to estimate the posterior probability of NLP annotations given their confidences (prior probabilities) and the ontological knowledge, and consequently revise the best annotation choice performed by the NLP tools. In a concrete scenario with two state-of-the-art tools for NERC and EL, we experimentally show on three reference datasets that for these tasks, the joint annotation revision performed by the model consistently improves on the original results of the tools.

scholarly journals Models and Processes to Extract Drug-like Molecules From Natural Language Text

2021 ◽  
Vol 8 ◽  
Author(s):  
Zhi Hong ◽  
J. Gregory Pauloski ◽  
Logan Ward ◽  
Kyle Chard ◽  
Ben Blaiszik ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
New Drugs ◽  
Training Data ◽  
Entity Recognition ◽  
Free Text ◽  
Natural Language Text ◽  
Named Entity ◽  
Iterative Model ◽  
Computational Screening ◽  
Promising Source

Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research. However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective. We report here on a project that leverages both human and artificial intelligence to detect references to such molecules in free text. We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to the problem of identifying drug-like molecules in the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198,875 papers. We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling time (tens of hours to label 1778 samples) to generate an NER model with an F-1 score of 80.5%—on par with that of non-expert humans—and when applied to CORD’19, identifies 10,912 putative drug-like molecules. This enriched the computational screening team’s targets by 3,591 molecules, of which 18 ranked in the top 0.1% of all 6.6 million molecules screened for docking against the 3CLPro protein.

Developing a RadLex-based Named Entity Recognition Tool for Mining Textual Radiology Reports (Preprint)

2020 ◽  
Author(s):  
Shintaro Tsuji ◽  
Andrew Wen ◽  
Naoki Takahashi ◽  
Hongjian Zhang ◽  
Katsuhiko Ogasawara ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Noun Phrases ◽  
General Purpose ◽  
Entity Recognition ◽  
Free Text ◽  
Clinical Text ◽  
Named Entity ◽  
Radiology Reports ◽  
Two Measures ◽  
F Measure

BACKGROUND Named entity recognition (NER) plays an important role in extracting the features of descriptions for mining free-text radiology reports. However, the performance of existing NER tools is limited because the number of entities depends on its dictionary lookup. Especially, the recognition of compound terms is very complicated because there are a variety of patterns. OBJECTIVE The objective of the study is to develop and evaluate a NER tool concerned with compound terms using the RadLex for mining free-text radiology reports. METHODS We leveraged the clinical Text Analysis and Knowledge Extraction System (cTAKES) to develop customized pipelines using both RadLex and SentiWordNet (a general-purpose dictionary, GPD). We manually annotated 400 of radiology reports for compound terms (Cts) in noun phrases and used them as the gold standard for the performance evaluation (precision, recall, and F-measure). Additionally, we also created a compound-term-enhanced dictionary (CtED) by analyzing false negatives (FNs) and false positives (FPs), and applied it for another 100 radiology reports for validation. We also evaluated the stem terms of compound terms, through defining two measures: an occurrence ratio (OR) and a matching ratio (MR). RESULTS The F-measure of the cTAKES+RadLex+GPD was 32.2% (Precision 92.1%, Recall 19.6%) and that of combined the CtED was 67.1% (Precision 98.1%, Recall 51.0%). The OR indicated that stem terms of “effusion”, "node", "tube", and "disease" were used frequently, but it still lacks capturing Cts. The MR showed that 71.9% of stem terms matched with that of ontologies and RadLex improved about 22% of the MR from the cTAKES default dictionary. The OR and MR revealed that the characteristics of stem terms would have the potential to help generate synonymous phrases using ontologies. CONCLUSIONS We developed a RadLex-based customized pipeline for parsing radiology reports and demonstrated that CtED and stem term analysis has the potential to improve dictionary-based NER performance toward expanding vocabularies.

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