Lecture

Text Understanding

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In course

DH-405: Foundations of digital humanities

This course introduces the core concepts and methodologies of Digital Humanities, integrating both theoretical and practical perspectives. Students learn to work with large-scale cultural datasets, acquiring the tools and techniques required for their proc ...

Description

This lecture delves into the concepts of Text Understanding, exploring the transition from Distant Reading to Machine Reading. It covers Close Reading, Surface Reading, and Distant Reading methods, emphasizing the importance of Named Entities and Information Extraction. The instructor discusses the evolution of Named Entity recognition, the role of knowledge bases, and the challenges in operationalizing text analysis. The lecture also touches on large-scale projects like Cyc and NELL, showcasing the potential of Open Information Extraction and the Never Ending Language Learning system.

Instructor

Frédéric Kaplan

Expertise Digital Humanities, Big Data, Models of language acquisition and evolution, Innovative Interfaces, Artificial Intelligence, Robotics, Developmental Systems, Intrinsic Motivation, Multi-agent system, Joint attention, Gesture Interaction Expertise Digital Humanities, Big Data, Models of language acquisition and evolution, Innovative Interfaces, Artificial Intelligence, Robotics, Developmental Systems, Intrinsic Motivation, Multi-agent system, Joint attention, Gesture Interaction Short biography Pro

Official source

Ontological neighbourhood

Information engineering

Natural language processing: Topics in natural language processing

Related concepts (31)

Natural-language understanding

Natural-language understanding (NLU) or natural-language interpretation (NLI) is a subtopic of natural-language processing in artificial intelligence that deals with machine reading comprehension. Natural-language understanding is considered an AI-hard problem. There is considerable commercial interest in the field because of its application to automated reasoning, machine translation, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.

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.

Natural language processing

Natural language processing (NLP) is an interdisciplinary subfield of linguistics and computer science. It is primarily concerned with processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them.

Knowledge base

A knowledge base (KB) is a set of sentences, each sentence given in a knowledge representation language, with interfaces to tell new sentences and to ask questions about what is known, where either of these interfaces might use inference. It is a technology used to store complex structured data used by a computer system. The initial use of the term was in connection with expert systems, which were the first knowledge-based systems. The original use of the term knowledge base was to describe one of the two sub-systems of an expert system.

Terminology extraction

Terminology extraction (also known as term extraction, glossary extraction, term recognition, or terminology mining) is a subtask of information extraction. The goal of terminology extraction is to automatically extract relevant terms from a given corpus. In the semantic web era, a growing number of communities and networked enterprises started to access and interoperate through the internet. Modeling these communities and their information needs is important for several web applications, like topic-driven web crawlers, web services, recommender systems, etc.

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Information Extraction: Methods and Applications

Explores methods for information extraction, including traditional and embedding-based approaches, supervised learning, distant supervision, and taxonomy induction.

Knowledge Inference: Entity Disambiguation and Graph Embeddings

Explores entity disambiguation, graph embeddings, scoring functions, and learning methods.

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Explores Semantic Web, ontologies, information extraction, key phrases, named entities, and knowledge bases.

Natural Language Processing Basics