# Conversation

1. [ConvoKit](https://github.com/CornellNLP/ConvoKit) by Cornell - is a toolkit for extracting conversational features and analyzing social phenomena in conversations. It includes several large conversational datasets along with scripts exemplifying the use of the toolkit on these datasets.
   1. #### [Politeness strategies](https://www.cs.cornell.edu/~cristian/Politeness.html) [(API)](https://convokit.cornell.edu/documentation/politenessStrategies.html)

      A set of lexical and parse-based features correlating with politeness and impoliteness.
   2. [Expected Conversational Context Framework](https://tisjune.github.io/research/dissertation) [(API)](https://convokit.cornell.edu/documentation/expected_context_model.html)

      A framework for characterizing utterances and terms based on their expected conversational context, consisting of model implementations and wrapper pipelines.&#x20;
   3. #### [Hypergraph conversation representation](http://www.cs.cornell.edu/~cristian/Patterns_of_participant_interactions.html) [(API)](https://convokit.cornell.edu/documentation/hyperconvo.html)

      A method for extracting structural features of conversations through a hypergraph representation.
   4. #### [Linguistic diversity in conversations](http://www.cs.cornell.edu/~cristian/Finding_your_voice__linguistic_development.html) [(API)](https://convokit.cornell.edu/documentation/speakerConvoDiversity.html)

      A method to compute the linguistic diversity of individuals within their own conversations, and between other individuals in a population.&#x20;

      <br>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://www.mlcompendium.com/natural-language-processing/conversation.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
