semantic-similarity | Semantic , sentimental , similarity analysis

by ml-distribution Java Version: v1.0.0 License: No License

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | semantic-similarity Summary

semantic-similarity is a Java library. semantic-similarity has build file available and it has low support. However semantic-similarity has 24 bugs and it has 18 vulnerabilities. You can download it from GitHub.

Semantic, sentimental, similarity analysis.

Support

Quality

Security

License

Reuse

Support

semantic-similarity has a low active ecosystem.

It has 56 star(s) with 38 fork(s). There are 22 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 998 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of semantic-similarity is v1.0.0

Quality

semantic-similarity has 24 bugs (0 blocker, 2 critical, 19 major, 3 minor) and 509 code smells.

Security

semantic-similarity has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

semantic-similarity code analysis shows 18 unresolved vulnerabilities (18 blocker, 0 critical, 0 major, 0 minor).

There are 9 security hotspots that need review.

License

semantic-similarity does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

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

semantic-similarity saves you 2944 person hours of effort in developing the same functionality from scratch.

It has 6355 lines of code, 419 functions and 91 files.

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

Top functions reviewed by kandi - BETA

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

Creates the panel
Gets the similarity between two strings
Get the edit distance
Segment a sentence
Creates the JPanel
Get the similarity between two sememes
Convenience method for comparing two lists
Returns a string representation of this object
Load sememes
Command for LCMC
Main entry point
Get the edit distance between two superstrings
Load sememe dictionary
Get the edit distance between two strings
Returns a string representation of this dictionary
Creates the main panel
Visit sememe list
Test the sentence
Returns the Levenshtein distance between two strings
Analyze a line
Save to xml
Read xml from xml file
Gets similarity
Compute similarity
Shortcut for testing
Parse define

Get all kandi verified functions for this library.

semantic-similarity Key Features

No Key Features are available at this moment for semantic-similarity.

semantic-similarity Examples and Code Snippets

No Code Snippets are available at this moment for semantic-similarity.

Community Discussions

Trending Discussions on semantic-similarity

How to do semantic matching in Python?

finding semantic similarity between 2 statements

Calculate Cross-Lingual Phrase Similarity (using e.g., MUSE and Gensim)

Spark(1.6) Densevector.type does not take parameters

QUESTION

How to do semantic matching in Python?

Asked 2021-Feb-03 at 10:30

Question:

I am trying to use semantic matching in Python on a group of words.

Sample Input: ...

ANSWER

Answered 2021-Feb-03 at 10:30

Looking at your input data, it seems that your goal is not semantic matching, but string matching. You can use fuzzywuzzy to do that:

Source https://stackoverflow.com/questions/66024397

QUESTION

finding semantic similarity between 2 statements

Asked 2020-Nov-08 at 21:56

I am currently working with small application in python and my application has search functionality (currently using difflib) but I want to create Semantic Search which can give top 5 or 10 results from my database, based on user inputted text. It is same as google search engine works. I found some solutions Here.

But the problem is, below two statements from one of solution are semantically incorrect. And I don't care about this. because they are making things too hard which I don't want And also solution will be some pretrained neural network model or library from which I can implement easily.

Pete and Rob have found a dog near the station.
Pete and Rob have never found a dog near the station

And also I found some solutions which are showing using gensim and Glove embeddings and finding similarity between words and not sentences.

What I wanted ?

Suppose my db has statement display classes and user inputs show, showed, displayed, displayed class, show types etc are same. And if above 2 statements are given as same then also I don't care. displayed and displayed class already showing in difflib.

Points to be noted

Find from fixed set of statements but user inputted statements can differ
Must work for statements

...

ANSWER

Answered 2020-Jun-01 at 11:58

You can use wordnet for finding synonyms and then use these synonyms for finding similar statements.

Source https://stackoverflow.com/questions/62131293

QUESTION

Calculate Cross-Lingual Phrase Similarity (using e.g., MUSE and Gensim)

Asked 2019-Oct-26 at 14:53

I am new to NLP and Word Embeddings and still need to learn many concepts within these topics, so any pointers would be appreciated. This question is related to this and this, and I think there may have been developments since these questions had been asked. Facebook MUSE provides aligned, supervised word embeddings for 30 languages, and it can be used to calculate word similarity across different languages. As far as I understand, The embeddings provided by MUSE satisfy the requirement of coordinate space compatibilty. It seems that it is possible to load these embeddings into libraries such as Gensim, but I wonder:

Is it possible to load multiple-language word embeddings into Gensim (or other libraries), and if so:
What type of similarity measure might fit in this use case?
How to use these loaded word embeddings to calculate cross-lingual similarity score of phrases* instead of words?

*e.g., "ÖPNV" in German vs "Trasporto pubblico locale" in Italian for the English term "Public Transport".

I am open o any implementation (libraries/languages/embeddings) though I may need some time to learn this topic. Thank you in advance.

...

ANSWER

Answered 2019-Oct-25 at 11:52

It is quite usual to average multiple word embeddings to get a phrase or sentence representation. After all, this is exactly what FastText does by default when it is used for sentence classification.

You can, of course, load as many word-embeddings sets in Gensim, but you would need to implement the cross-lingual comparison yourself. You can the vector just using the square bracket notation:

Source https://stackoverflow.com/questions/58556924

QUESTION

Spark(1.6) Densevector.type does not take parameters

Asked 2017-Oct-26 at 13:02

I'm following along with this article:

http://learningfrombigdata.com/semantic-similarity-between-sentences-using-apache-spark/

However, when i get to this part:

...

ANSWER

Answered 2017-Oct-26 at 13:02

Missing new:

Source https://stackoverflow.com/questions/46954143

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

Vulnerabilities

No vulnerabilities reported

Install semantic-similarity

You can download it from GitHub.
You can use semantic-similarity like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the semantic-similarity component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

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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