nlp

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Selected Machine Learning algorithms for natural language processing and semantic analysis in Golang For more information about how to use this package see README

Latest version published 3 years ago

GitHub

Package Health Score

55 / 100

security
No known security issues
popularity
Popular
maintenance
Inactive
community
Sustainable

Popularity

Popular

Imported By: 21
GitHub Stars: 431
Forks: 45
Contributors: 5

Usage Popularity

Based on project statistics from the GitHub repository for the Golang package nlp, we found that it has been 431 times.

The popularity score for Golang modules is calculated based on the number of stars that the project has on GitHub as well as the number of imports by other modules.

Security

No known security issues

Security risks

Direct Vulnerabilities: 0
C
0
H
0
M
0
L

Indirect Vulnerabilities: Check with Snyk

Release Date

May 11, 2021

License

MIT

Security Policy

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Maintenance

Inactive

Commit Frequency

No Recent Commits

Open Issues: 5
Open PR: 0
Last Release: 3 years ago
Last Commit: 3 years ago

Further analysis of the maintenance status of github.com/james-bowman/nlp based on released golang versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for github.com/james-bowman/nlp is that it hasn't seen any new versions released to golang in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Sustainable

Readme.md: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 5
Funding: No

This project has seen only 10 or less contributors.

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Readme

Natural Language Processing

Implementations of selected machine learning algorithms for natural language processing in golang. The primary focus for the package is the statistical semantics of plain-text documents supporting semantic analysis and retrieval of semantically similar documents.

Built upon the Gonum package for linear algebra and scientific computing with some inspiration taken from Python's scikit-learn and Gensim.

Check out the companion blog post or the Go documentation page for full usage and examples.

Features

LSA (Latent Semantic Analysis aka Latent Semantic Indexing (LSI)) implementation using truncated SVD (Singular Value Decomposition) for dimensionality reduction.
Fast comparison and retrieval of semantically similar documents using SimHash(random hyperplanes/sign random projection) algorithm with multi-index and Forest schemes for LSH (Locality Sensitive Hashing) to support fast, approximate cosine similarity/angular distance comparisons and approximate nearest neighbour search using significantly less memory and processing time.
Random Indexing (RI) and Reflective Random Indexing (RRI) (which extends RI to support indirect inference) for scalable Latent Semantic Analysis (LSA) over large, web-scale corpora.
Latent Dirichlet Allocation (LDA) using a parallelised implementation of the fast SCVB0 (Stochastic Collapsed Variational Bayesian inference) algorithm for unsupervised topic extraction.
PCA (Principal Component Analysis)
TF-IDF weighting to account for frequently occuring words
Sparse matrix implementations used for more efficient memory usage and processing over large document corpora.
Stop word removal to remove frequently occuring English words e.g. "the", "and"
Feature hashing ('the hashing trick') implementation (using MurmurHash3) for reduced memory requirements and reduced reliance on training data
Similarity/distance measures to calculate the similarity/distance between feature vectors.

Planned

Expanded persistence support
Stemming to treat words with common root as the same e.g. "go" and "going"
Clustering algorithms e.g. Heirachical, K-means, etc.
Classification algorithms e.g. SVM, KNN, random forest, etc.

References

FAQs

What is nlp?

Selected Machine Learning algorithms for natural language processing and semantic analysis in Golang. Visit Snyk Advisor to see a full health score report for nlp, including popularity, security, maintenance & community analysis.

Is nlp popular?

The golang package nlp receives a total of ? weekly downloads. As such, nlp popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is nlp well maintained?

We found indications that nlp is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is nlp safe to use?

The golang package nlp was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 30 April-2024, at 15:50 (UTC).

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