pywsd | Python Implementations of Word Sense Disambiguation | Natural Language Processing library

by alvations Python Version: 1.2.5 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | pywsd Summary

pywsd is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. pywsd has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install pywsd' or download it from GitHub, PyPI.

Python Implementations of Word Sense Disambiguation (WSD) technologies:.

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Security

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Support

pywsd has a low active ecosystem.

It has 653 star(s) with 122 fork(s). There are 42 watchers for this library.

It had no major release in the last 12 months.

There are 15 open issues and 38 have been closed. On average issues are closed in 206 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pywsd is 1.2.5

Quality

pywsd has 0 bugs and 0 code smells.

Security

pywsd has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pywsd code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pywsd 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

pywsd releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

pywsd saves you 533 person hours of effort in developing the same functionality from scratch.

It has 1245 lines of code, 74 functions and 30 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed pywsd and discovered the below as its top functions. This is intended to give you an instant insight into pywsd implemented functionality, and help decide if they suit your requirements.

Disambiguate a sentence
Lemmatize an ambiguous word
Lemmatize a sentence
Convert pen to morphy tag
Compute cosine similarity
Return the signatures of a Synset
Return the synset signatures for the given ambiguous word
Given an ambiguous word and pos
Calculate the maximum similarity between two sentences
Compute similarity between two words
Return the similarity between two senses
Compute similarity between two senses
Create a Synset from an ambiguous sentence
Given a list of synsets returns a list of synnsets
Compute the similarity between two synset words
Returns test instances
Get answers from test_ans
Yield sentences from the test file
Remove tags from text
Computes the lemmatize of an ambiguous word
Computes the synset with the semantics of the context
Return a random sense
Return first sense from ambiguous word
Evaluate the model
Add intercept term to the array
Return a list of synsets associated with a word

Get all kandi verified functions for this library.

pywsd Key Features

No Key Features are available at this moment for pywsd.

pywsd Examples and Code Snippets

No Code Snippets are available at this moment for pywsd.

Community Discussions

Trending Discussions on pywsd

Word-sense disambiguation based on sets of words using pre-trained embeddings

QUESTION

Word-sense disambiguation based on sets of words using pre-trained embeddings

Asked 2020-Aug-11 at 13:42

I am interested in identifying the WordNet synset IDs for each word in a set of tags. The words in the set provide the context for the word sense disambiguation, such as:

{mole, skin}
{mole, grass, fur}
{mole, chemistry}
{bank, river, river bank}
{bank, money, building}

I know of the lesk algorithm and libraries, such as pywsd, which is based on 10+ year old tech (which may still be cutting edge -- that is my question).

Are there better performing algorithms by now that make sense of pre-trained embeddings, like GloVe, and maybe the distances of these embeddings to each other? Are there ready-to-use implementations of such WSD algorithms?

I know this question is close to the danger zone of asking for subjective preferences - as in this 5-year old thread. But I am not asking for an overview of options or the best software for a problem.

...

ANSWER

Answered 2020-Aug-11 at 13:42

Transfer learning, particularly models like Allen AI’s ELMO, OpenAI’s Open-GPT, and Google’s BERT allowed researchers to smash multiple benchmarks with minimal task-specific fine-tuning and provided the rest of the NLP community with pretrained models that could easily (with less data and less compute time) be fine-tuned and implemented to produce state of the art results.

these representations will help you accuratley retrieve results matching the customer's intent and contextual meaning(), even if there's no keyword or phrase overlap.

To start off, embeddings are simply (moderately) low dimensional representations of a point in a higher dimensional vector space.

By translating a word to an embedding it becomes possible to model the semantic importance of a word in a numeric form and thus perform mathematical operations on it.

When this was first possible by the word2vec model it was an amazing breakthrough. From there, many more advanced models surfaced which not only captured a static semantic meaning but also a contextualized meaning. For instance, consider the two sentences below:

Source https://stackoverflow.com/questions/63348829

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

Vulnerabilities

No vulnerabilities reported

Install pywsd

You can install using 'pip install pywsd' or download it from GitHub, PyPI.
You can use pywsd like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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