Methods Lab / NLP001
NLP: from language to research applications
Natural language processing (NLP) uses computational methods to work with human language. Systems use explicit rules or learn patterns from data to analyze, extract, predict, and generate information.
Large language models are one family of technologies within this field. [1]
The big picture
Visual notes
01Information in language
| Perspective | What to look for | Example |
|---|---|---|
| Word structure and meaning | How words are formed and what they mean | happy → unhappy; “satisfied” versus “not satisfied” |
| Sentence structure and meaning | Roles, evaluation targets, and contrasts | “The explanation was clear, but the wait was too long” evaluates two things differently |
| Context and intent | References across sentences and the purpose of an utterance | “These instructions are confusing” may be a request for clarification |
These perspectives help explain language; they are not mandatory sequential stages in every model. [1][2]
A conceptual illustration, not a fixed architecture inside a model. NLU and NLG need not follow strictly inverse pipelines, and context matters across perspectives.
02Speech analysis: how something is said
| Cue | What it captures |
|---|---|
| Loudness | How loud or soft the voice is, and how it changes |
| Speaking rate | How quickly or slowly someone speaks |
| Delivery | Intonation, stress, pauses, and related patterns |
Interpret acoustic and prosodic cues alongside the words and the situation. A plain transcript usually does not preserve all of them; analysis needs audio or the relevant acoustic features. Speech and text analysis can be combined, but measuring vocal features is different from interpreting linguistic content. [3]
03Common tasks
| Task | The question it addresses |
|---|---|
| Sentiment analysis | What is being evaluated positively, negatively, or in mixed terms? |
| Emotion cue recognition | What feelings are expressed or implied? A cue is not a clinical diagnosis |
| Information extraction | Which themes, entities, or events should become fields or labels linked to the source? |
| Summarization | What are the main points of an interview, meeting, or body of material? Preserve important contrasts and uncertainty |
| Translation and question answering | How can information be expressed in another language, or used to answer a question? |
One passage can support several tasks.
“The explanation was clear, but the wait was too long.”
Sentiment analysis
Praise for the explanation; frustration with the wait
Information extraction
Explanation quality and waiting experience
Summarization
Preserve both judgments
Summarization concerns the key content, while speech analysis examines vocal cues. A workflow can use both.
04My perspective & use case
My starting point
Language itself makes NLP more approachable. Connecting words, sentences, and context to specific tasks helps me understand the broader framework.
My research direction
I have worked with interviews by manually marking passages and extracting keywords. I want to explore NLP-assisted organization and summaries of participants’ main points.
- Research question
- Information needed
- Fields or labels
- Source check
Usefulness depends on omissions, label quality, and time saved after corrections.
05Handwritten notes
A working note on language features, common tasks, and vocal expression.
06References
- Jurafsky & Martin — Speech and Language Processing
- Words and Tokens
- audEERING — Prosody & speech features
Its findings concern particular models and a specific task; they do not validate an interview-analysis workflow.