scholarly journals Applications of Natural Language Processing in Biodiversity Science

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Anne E. Thessen ◽  
Hong Cui ◽  
Dmitry Mozzherin
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Large Scale ◽  
Morphological Characters ◽  
Machine Learning Algorithms ◽  
Biological Information ◽  
Biological Knowledge ◽  
Biodiversity Science

Centuries of biological knowledge are contained in the massive body of scientific literature, written for human-readability but too big for any one person to consume. Large-scale mining of information from the literature is necessary if biology is to transform into a data-driven science. A computer can handle the volume but cannot make sense of the language. This paper reviews and discusses the use of natural language processing (NLP) and machine-learning algorithms to extract information from systematic literature. NLP algorithms have been used for decades, but require special development for application in the biological realm due to the special nature of the language. Many tools exist for biological information extraction (cellular processes, taxonomic names, and morphological characters), but none have been applied life wide and most still require testing and development. Progress has been made in developing algorithms for automated annotation of taxonomic text, identification of taxonomic names in text, and extraction of morphological character information from taxonomic descriptions. This manuscript will briefly discuss the key steps in applying information extraction tools to enhance biodiversity science.

scholarly journals Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing

2019 ◽  
Vol 45 (3) ◽  
pp. 559-601 ◽  
Author(s):  
Edoardo Maria Ponti ◽  
Helen O’Horan ◽  
Yevgeni Berzak ◽  
Ivan Vulić ◽  
Roi Reichart ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Extensive Literature ◽  
New Approach ◽  
Recent Developments ◽  
Discrete Nature

Linguistic typology aims to capture structural and semantic variation across the world’s languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-utilization of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such an approach could be facilitated by recent developments in data-driven induction of typological knowledge.

Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.

2021 ◽  
Author(s):  
Xinxu Shen ◽  
Troy Houser ◽  
David Victor Smith ◽  
Vishnu P. Murty
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Individual Difference ◽  
Language Processing ◽  
Large Scale ◽  
High Reliability ◽  
Difference Analysis ◽  
Universal Sentence ◽  
Natural Language Processing Tool

The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for naturalistic events. However, scoring narrative recalls is time-consuming and prone to human biases. Here, we show the validity and reliability of using a natural language processing tool, the Universal Sentence Encoder (USE), to automatically score narrative recall. We compared the reliability in scoring made between two independent raters (i.e., hand-scored) and between our automated algorithm and individual raters (i.e., automated) on trial-unique, video clips of magic tricks. Study 1 showed that our automated segmentation approaches yielded high reliability and reflected measures yielded by hand-scoring, and further that the results using USE outperformed another popular natural language processing tool, GloVe. In study two, we tested whether our automated approach remained valid when testing individual’s varying on clinically-relevant dimensions that influence episodic memory, age and anxiety. We found that our automated approach was equally reliable across both age groups and anxiety groups, which shows the efficacy of our approach to assess narrative recall in large-scale individual difference analysis. In sum, these findings suggested that machine learning approaches implementing USE are a promising tool for scoring large-scale narrative recalls and perform individual difference analysis for research using naturalistic stimuli.

scholarly journals Natural language processing for automated annotation of medication mentions in primary care visit conversations

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Craig H Ganoe ◽  
Weiyi Wu ◽  
Paul J Barr ◽  
William Haslett ◽  
Michelle D Dannenberg ◽  
...  
Keyword(s):  
Primary Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Primary Care Visit ◽  
Data Set ◽  
Test Set ◽  
Medication Information ◽  
Care Visit

Abstract Objectives The objective of this study is to build and evaluate a natural language processing approach to identify medication mentions in primary care visit conversations between patients and physicians. Materials and Methods Eight clinicians contributed to a data set of 85 clinic visit transcripts, and 10 transcripts were randomly selected from this data set as a development set. Our approach utilizes Apache cTAKES and Unified Medical Language System controlled vocabulary to generate a list of medication candidates in the transcribed text and then performs multiple customized filters to exclude common false positives from this list while including some additional common mentions of the supplements and immunizations. Results Sixty-five transcripts with 1121 medication mentions were randomly selected as an evaluation set. Our proposed method achieved an F-score of 85.0% for identifying the medication mentions in the test set, significantly outperforming existing medication information extraction systems for medical records with F-scores ranging from 42.9% to 68.9% on the same test set. Discussion Our medication information extraction approach for primary care visit conversations showed promising results, extracting about 27% more medication mentions from our evaluation set while eliminating many false positives in comparison to existing baseline systems. We made our approach publicly available on the web as an open-source software. Conclusion Integration of our annotation system with clinical recording applications has the potential to improve patients’ understanding and recall of key information from their clinic visits, and, in turn, to positively impact health outcomes.

scholarly journals Designing and Validating an Annotation Model of Dynamic Modality for English and Spanish: Issues and Problems

10.29007/pc58 ◽  
2018 ◽  
Author(s):  
Julia Lavid ◽  
Marta Carretero ◽  
Juan Rafael Zamorano
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Reliability Study ◽  
Annotation Scheme ◽  
High Degree ◽  
Difficult Cases

In this paper we set forth an annotation model for dynamic modality in English and Spanish, given its relevance not only for contrastive linguistic purposes, but also for its impact on practical annotation tasks in the Natural Language Processing (NLP) community. An annotation scheme is proposed, which captures both the functional-semantic meanings and the language-specific realisations of dynamic meanings in both languages. The scheme is validated through a reliability study performed on a randomly selected set of one hundred and twenty sentences from the MULTINOT corpus, resulting in a high degree of inter-annotator agreement. We discuss our main findings and give attention to the difficult cases as they are currently being used to develop detailed guidelines for the large-scale annotation of dynamic modality in English and Spanish.

scholarly journals Comparison of Templates with Word2vec in Finding Semantic Relations Between Words

2018 ◽  
pp. 13-17
Author(s):  
Kaan Ant ◽  
Ugur Sogukpinar ◽  
Mehmet Fatif Amasyali
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Semantic Relations ◽  
Template Method ◽  
Semantic Relationships ◽  
Semantic Spaces

The use of databases those containing semantic relationships between words is becoming increasingly widespread in order to make natural language processing work more effective. Instead of the word-bag approach, the suggested semantic spaces give the distances between words, but they do not express the relation types. In this study, it is shown how semantic spaces can be used to find the type of relationship and it is compared with the template method. According to the results obtained on a very large scale, while is_a and opposite are more successful for semantic spaces for relations, the approach of templates is more successful in the relation types at_location, made_of and non relational.

scholarly journals A Comparative Analysis of Machine Learning Techniques for Spam Detection

2021 ◽  
pp. 657-661
Author(s):  
Rashida Ali ◽  
Ibrahim Rampurawala ◽  
Mayuri Wandhe ◽  
Ruchika Shrikhande ◽  
Arpita Bhatkar
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Comparative Analysis ◽  
Natural Language ◽  
Language Processing ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Spam Detection ◽  
Learning Techniques

Internet provides a medium to connect with individuals of similar or different interests creating a hub. Since a huge hub participates on these platforms, the user can receive a high volume of messages from different individuals creating a chaos and unwanted messages. These messages sometimes contain a true information and sometimes false, which leads to a state of confusion in the minds of the users and leads to first step towards spam messaging. Spam messages means an irrelevant and unsolicited message sent by a known/unknown user which may lead to a sense of insecurity among users. In this paper, the different machine learning algorithms were trained and tested with natural language processing (NLP) to classify whether the messages are spam or ham.

Abstract 165: Automated Stroke-Related Information Extraction From Diagnostic Imaging Reports Using Natural Language Processing

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Zhongyu Anna Liu ◽  
Muhammad Mamdani ◽  
Richard Aviv ◽  
Chloe Pou-Prom ◽  
Amy Yu
Keyword(s):  
Natural Language Processing ◽  
Diagnostic Imaging ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Ct Perfusion ◽  
Training Sample ◽  
Free Text ◽  
Validation Set ◽  
Proximal Occlusion

Introduction: Diagnostic imaging reports contain important data for stroke surveillance and clinical research but converting a large amount of free-text data into structured data with manual chart abstraction is resource-intensive. We determined the accuracy of CHARTextract, a natural language processing (NLP) tool, to extract relevant stroke-related attributes from full reports of computed tomograms (CT), CT angiograms (CTA), and CT perfusion (CTP) performed at a tertiary stroke centre. Methods: We manually extracted data from full reports of 1,320 consecutive CT/CTA/CTP performed between October 2017 and January 2019 in patients presenting with acute stroke. Trained chart abstractors collected data on the presence of anterior proximal occlusion, basilar occlusion, distal intracranial occlusion, established ischemia, haemorrhage, the laterality of these lesions, and ASPECT scores, all of which were used as a reference standard. Reports were then randomly split into a training set (n= 921) and validation set (n= 399). We used CHARTextract to extract the same attributes by creating rule-based information extraction pipelines. The rules were human-defined and created through an iterative process in the training sample and then validated in the validation set. Results: The prevalence of anterior proximal occlusion was 12.3% in the dataset (n=86 left, n=72 right, and n=4 bilateral). In the training sample, CHARTextract identified this attribute with an overall accuracy of 97.3% (PPV 84.1% and NPV 99.4%, sensitivity 95.5% and specificity 97.5%). In the validation set, the overall accuracy was 95.2% (PPV 76.3% and NPV 98.5%, sensitivity 90.0% and specificity 96.0%). Conclusions: We showed that CHARTextract can identify the presence of anterior proximal vessel occlusion with high accuracy, suggesting that NLP can be used to automate the process of data collection for stroke research. We will present the accuracy of CHARTextract for the remaining neurological attributes at ISC 2020.

Natural Language Processing-Based Information Extraction and Abstraction for Lease Documents

2020 ◽  
pp. 170-187
Author(s):  
Sumathi S. ◽  
Rajkumar S. ◽  
Indumathi S.
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Data Extraction ◽  
Easy Access ◽  
Property A ◽  
Key Events

Lease abstraction is the method of compartmentalization of key data from a lease document. Lease document for a property contains key business, money, and legal data about a property. A lease abstract report contains details concerning the property location and basic lease details, price schedules, key events, terms and conditions, automobile parking arrangements, and landowner and tenant obligations. Abstracting a true estate contract into electronic type facilitates easy access to key data, exchanging the tedious method of reading the whole contents of the contract every time. Language process may be used for data extraction and abstraction of knowledge from lease documents.

Sign in / Sign up

Export Citation Format

Share Document