IARM | Aspect Relation Modeling with Memory Networks | Predictive Analytics library

by SenticNet Python Version: Current License: MIT

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kandi X-RAY | IARM Summary

IARM is a Python library typically used in Analytics, Predictive Analytics, Deep Learning, Pytorch, Tensorflow, Neural Network applications. IARM has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However IARM build file is not available. You can download it from GitHub.

This repo contains the source code of the paper --. IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis. Navonil Majumder, Soujanya Poria, Alexander Gelbukh, Md. Shad Akhtar, Erik Cambria, Asif Ekbal. EMNLP 2018. This method attempts to model the relationship among the different aspect-terms in a sentence using memory networks to enable better sentiment classification of the aspects.

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Quality

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Support

IARM has a low active ecosystem.

It has 43 star(s) with 16 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

IARM has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of IARM is current.

Quality

IARM has 0 bugs and 0 code smells.

Security

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

IARM code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

IARM 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

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

IARM has no build file. You will be need to create the build yourself to build the component from source.

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

IARM saves you 144 person hours of effort in developing the same functionality from scratch.

It has 361 lines of code, 17 functions and 1 files.

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

Top functions reviewed by kandi - BETA

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

Compute the attention layer .
Prepare data .
Train Keras model
Test the model .
Initialize the network .
Reads a csv file .
Extract word embeddings .
Computes the accuracy of the truth test .
Compute embedding matrix for word embeddings .
Prepare keras data .

Get all kandi verified functions for this library.

IARM Key Features

No Key Features are available at this moment for IARM.

IARM Examples and Code Snippets

No Code Snippets are available at this moment for IARM.

Community Discussions

Trending Discussions on IARM

Why can't I parse XML?

QUESTION

Why can't I parse XML?

Asked 2020-Oct-15 at 06:35

I'm getting errors parsing the result message to XML. Does anyone knows why?

Thank you.

...

ANSWER

Answered 2020-Oct-15 at 04:32

Because in your input all " characters have been replaced by \" to be able to store it in an other string, this makes for incorrect attributes in your XML.

A quick fix if the input can't be fixed, consists in parsing the resulting string, wrapped by two ".

Source https://stackoverflow.com/questions/64364599

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

Vulnerabilities

No vulnerabilities reported

Install IARM

You can download it from GitHub.
You can use IARM 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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