Questions and Answers
What is the main focus of this course?
What is an example of an application that uses text as input and output?
What is NOT a name for this field of computer science?
What is an example of a text input?
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What makes language more complex than just following rules?
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What is an example of a task that can be achieved with text as input?
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What is NOT an application of using text as input?
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What is an example of a source of text input?
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What is the benefit of new CPUs and GPUs in the field of Text as Data?
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What is a characteristic of the ELMo and BERT deep learning approaches?
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What is an example of a high-profile product that uses advanced language models?
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What is a way that language models are typically trained?
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What is a benefit of the internet for language researchers?
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What is an example of a creative application of advanced language models?
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What is a current research area that building with linguistics research is tied to?
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What is a task that language models are typically trained to perform?
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What is a key characteristic of transformer-based models in the context of language understanding?
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What is a concern related to bigger language models?
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What should you be cautious of when it comes to claims of language models 'understanding' text?
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What is a recent development in the field of text as data?
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What is an application of language models?
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What is a characteristic of the field of text as data?
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What is an example of a language model that has shown impressive abilities in text generation?
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What is a benefit of language models in terms of input and output?
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What is a primary reason why computers need to work with text?
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Which of these examples best demonstrates text being used as both input and output for a computer?
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Why is unstructured text data challenging to process?
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What is a key implication of the rapidly growing amount of text data?
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Which of the following best describes the use of a language model in the context of text processing?
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How do Transformer models differ from traditional recurrent neural networks (RNNs) for natural language processing?
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Which of the following deep learning architectures is commonly used for text generation tasks?
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What is a key challenge in developing language models that can understand and interpret the meaning of text?
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What is the primary purpose of using bi-grams and tri-grams in text analysis?
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What is a common challenge when splitting text into words during tokenization?
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Which of the following best describes stemming in the context of text processing?
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How do stopwords impact the effectiveness of text analysis?
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In which scenario would lemmatization be preferred over stemming?
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What is the initial step in a standard text analysis pipeline?
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Why is it important to use metrics to weigh rarer words more heavily?
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What is a significant limitation of character-based analysis in natural language processing?
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Study Notes
What is Text as Data?
- Also known as Natural Language Processing (NLP), Computational Linguistics, and Text Analytics
- Involves working with text as input, output, or both
- Text data is unstructured, growing rapidly, and hard to process
Text as Input
- Examples: documents, tweets, voice commands, search queries, web pages, medical records, books
- Applications: news aggregation, search tools, email suggestions
Text as Output
- Examples: basic text output with rules (e.g., generating numbers with rules), advanced text output (e.g., creative writing)
- Applications: assistants (e.g., Siri, Alexa), machine translation, email suggestions, text adventure games
Text as Data History and Future
- Built on linguistics research (e.g., how language works, how we learn language)
- Tied to computational performance (e.g., new CPUs and GPUs enable advances)
- The internet provides an incredible source of example text
- Deep learning is changing the approach, making it a fast-moving field
New Language Systems and Abilities
- Trained by asking them to complete a sentence
- Developed models like ELMo and BERT, which can succeed at several different problems
- Can find similar documents (e.g., document similarity task)
Course Introduction
- Why computers need to work with text: humans interact with language, and language can be represented as text
- Overview of the course: what we will learn, practicalities (e.g., labs, assessments)
- Importance of working with text: text data is growing rapidly, and computers may use text as input, output, or both
Text Data and Deep Learning
- Text data is ever-growing, and we need to work with it
- BERT model showed incredible new abilities
- Deep learning has had a significant impact on the field, with transformers and language models
- However, bigger models come with huge costs (e.g., training, computational, data, environmental)
Summary of Text as Data Introduction
- Computers use text as input and output
- Amount of text data is growing rapidly
- Deep learning has had a significant impact on the field
- Field is very fast-moving
- Importance of being skeptical of AI "understanding" text claims
Studying That Suits You
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Description
Learn about Text as Data, also known as Natural Language Processing (NLP), and its applications in working with unstructured text data. Explore examples of text as input and output.