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DTSTART:20251103T120000Z
DTEND:20251103T150000Z
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SUMMARY:Syntactic parsing of natural language in Python: Session 3/4 in Learning Python for text-mining and the analysis of natural language
DESCRIPTION:Users with access to the gROW learning environment (those with 
 a U/E-number) can find more information about workshops and courses offered 
 by the Radboud Digital Competence Centre here. The gROW environment is the 
 official location for these materials\, and gROW is preferred to libcal for 
 sign-ups and for exploring our materials. Follow this link to go directly 
 to the gROW page for the current workshop series. Users who are unable to 
 access gROW are welcome to register using libcal. A overview of the 
 currently scheduled digital methods trainings can be found on libcal after 
 filtering by the category "DCC" (linked here for convenience). \n\nSeries 
 overview:\n\nText-mining refers to techniques which can involve the 
 collection\, processing and parsing of text derived from a range of sources 
 (e.g.\, corpora\, digital libraries\, web forums). Text-mining is often 
 performed with the goal of analyzing text to gain insights not readily 
 available without the use of digital methods. It is useful to or shares 
 methods with fields which seek to understand words and their context using 
 computer friendly representations (e.g.\, word embeddings\, or word 
 vectors) such as natural language processing\, computational models of 
 language\, psycholinguistcs\, or digital humanities. In this workshop 
 series\, students and researchers will learn text-mining and natural 
 language processing (NLP) techniques using the Python programming language 
 for a range of use-cases. Four workshop sessions are currently available. 
 Participants may choose to attend whichever of these self-contained 
 sessions they find useful\, but it is recommended that participants who are 
 new to this topic attend at least Session 2. Readers who are interested in 
 learning about additional tools and methods for text-mining and 
 computer-based analysis of natural language are encouraged to consult the 
 recently published text-mining guide\, written by the text-mining support 
 group at Information & Library Services (textminingsupport@ru.nl). 
 Questions about these workshops or their contents can be sent via email to 
 daniel.sharoh@ru.nl.\n\n****************\n\nSession 3: Syntactic parsing of 
 natural language in Python\n\nDo you want to learn how to use Python to 
 parse the syntactic structure of natural language? The Digital Competence 
 Centre organizes a workshop session on this topic which is suitable for 
 participants with a broad range of skill-levels.\n\nNatural languages such 
 as English and Dutch can be analyzed in terms of both their syntactic and 
 semantic properties. More "semantic" techniques such as sentiment analysis 
 often analyze words and their context to calculate the "sentiment" of a 
 news article or piece of text\, but considerable information can also be 
 derived from the syntactic structure of sentences. In this workshop\, 
 participants will learn to parse (e.g. derive syntactic trees)\, from 
 sample text in Dutch or English that has been prepared for this workshop. 
 Participants will also learn to visualize these parses using libraries in 
 Python (and Latex). The ultimate goal of the workshop is to produce a 
 simple analysis of the syntactic complexity of workshop-provided 
 sentences\, which can then be used for a number of down-stream analyses. 
 For example: analyzing student essays\, predicting response reaction time 
 for experimental stimuli\, or assessing readability. Similar analyses might 
 also be performed to understand the distribution of structure types in a 
 given text (e.g. active v. passive voice\, double object datives). This 
 session is therefore useful for anyone who would like to analyze the 
 syntactic properties of text at large or small scales.\n\nPlease follow the 
 links below to sign-up for the other sessions:\n\nLink to Session 1\n\nLink 
 to Session 2\n\nLink to Session 4
LOCATION:UBN 1.40E \, Central Library 
ORGANIZER;CN="Daniel Sharoh":MAILTO:Daniel.Sharoh@ru.nl
CATEGORIES:DCC, Text Mining
CONTACT;CN="Daniel Sharoh":MAILTO:Daniel.Sharoh@ru.nl
STATUS:CONFIRMED
UID:LibCal-4413586
URL:https://libcal.ru.nl/event/4413586
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