broader-metaphor | Code and data for Learning Outside the Box

by jayelm HTML Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | broader-metaphor Summary

broader-metaphor is a HTML library. broader-metaphor has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Code and data for "Learning Outside the Box: Discourse-level Features Improve Metaphor Identification", NAACL 2019

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Quality

Security

License

Reuse

Support

broader-metaphor has a low active ecosystem.

It has 22 star(s) with 5 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of broader-metaphor is current.

Quality

broader-metaphor has no bugs reported.

Security

broader-metaphor has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

broader-metaphor is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

broader-metaphor releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

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broader-metaphor Key Features

No Key Features are available at this moment for broader-metaphor.

broader-metaphor Examples and Code Snippets

No Code Snippets are available at this moment for broader-metaphor.

Community Discussions

No Community Discussions are available at this moment for broader-metaphor.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install broader-metaphor

Each folder has a corresponding README with more details.
analysis/ contains dev set predictions made by the models in the paper, as well as an RMarkdown script which investigates the ELMo model specifically.
data/ contains the original VUAMC.xml file, and vuamc.csv,a processed version with additional context + arguments. For details on how to reproduce VUAMC.csv, see data/README.md.
features/ is empty and will contain the extracted model features generated by the extract_*.py scripts. Contact me if you're lazy and just want to run classification with the features outright.
models/ is empty and will contain pretrained doc2vec and skip-thought models if you want to reproduce those results. (Pretrained embeddings for GloVe and ELMo are handled by spaCy and allennlp, respectively; see below)
skip-thoughts/ just links to rkiros/skipthoughts.
classify.py is the main XGBoost classification script.
extract_*.py are the scripts used to generate classification features.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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