Coffee and Code: Natural Language Processing with Python

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The processing and analysis of natural languages is a core requirement for extracting structured information from spoken, signed, or written language and for feeding that information into systems or processes that generate insights from, or responses to provided language data. As languages that are naturally evolved and not designed for a specific purpose natural languages pose significant challenges when developing automated systems.

Natural Language Processing - the class of activities in which language analysis, interpretation, and generation play key roles - is used in many disciplines as is demonstrated by this random sample of recent papers using NLP to address very different research problems:

"Unsupervised entity and relation extraction from clinical records in Italian" (1)
"Candyflipping and Other Combinations: Identifying Drug–Drug Combinations from an Online Forum" (2)
"How Can Linguistics Help to Structure a Multidisciplinary Neo Domain such as Exobiology?" (3)
"Bag of meta-words: A novel method to represent document for the sentiment classification" (4)
"Information Needs and Communication Gaps between Citizens and Local Governments Online during Natural Disasters" (5)
"Mining the Web for New Words: Semi-Automatic Neologism Identification with the NeoCrawler" (6)
"Distributed language representation for authorship attribution" (7)
"Toward a computational history of universities: Evaluating text mining methods for interdisciplinarity detection from PhD dissertation abstracts" (8)
"Ecological momentary interventions for depression and anxiety" (9)

Authoring Person(s) Name: 
Karl Benedict
Authoring Organization(s) Name: 
University of New Mexico Research Data Services
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No fee
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Data analysis
Data coding
Natural language processing
Published / Broadcast: 
Friday, October 19, 2018
Publisher - organization credited with publishing or broadcasting the learning resource: 
University of New Mexico Research Data Services
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Contact Person(s): 
Karl Benedict
Contact Organization(s): 
University of New Mexico Research Data Services
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Purpose - primary educational reason for which the learning resource was created: 
Instruction - detailed information about aspects or processes related to data management or data skills.
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Learning Activity - guided or unguided activity engaged in by a learner to acquire skills, concepts, or knowledge that may or may not be defined by a lesson. Examples: data exercises, data recipes.
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Data professional
Graduate student
Undergraduate student
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
More than 1 hour (but less than 1 day)