Lecture

Digital Humanities Internship Experience

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Description

This lecture presents the internship experience of a Digital Humanities Master student at EPFL, who joined the RTS AI team to work on extracting data from video archives using machine learning technologies. The student conducted a literature review, selected an algorithm, created a dataset, and implemented the algorithm to develop a video search engine. The lecture highlights the challenges faced, skills utilized, and the impact of the project on the RTS staff.

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Related concepts (7)

Knowledge extraction

Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, s) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL (data warehouse), the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema.

Information extraction

Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video/documents could be seen as information extraction Due to the difficulty of the problem, current approaches to IE (as of 2010) focus on narrowly restricted domains.

Data scraping

Data scraping is a technique where a computer program extracts data from human-readable output coming from another program. Normally, data transfer between programs is accomplished using data structures suited for automated processing by computers, not people. Such interchange and protocols are typically rigidly structured, well-documented, easily parsed, and minimize ambiguity. Very often, these transmissions are not human-readable at all.

Data extraction

Data extraction is the act or process of retrieving data out of (usually unstructured or poorly structured) data sources for further data processing or data storage (data migration). The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another stage in the data workflow. Usually, the term data extraction is applied when (experimental) data is first imported into a computer from primary sources, like measuring or recording devices.

Data mining

Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.

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