Series overview:

Text-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.

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Session 3: Syntactic parsing of natural language in Python

Do 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.

Natural 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 readibility. 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.