lemmer | English Lemmer interface for Node.js | Runtime Evironment library

by superscriptjs JavaScript Version: 0.3.0 License: MIT

X-Ray Key Features Code Snippets Community Discussions(8)Vulnerabilities Install Support

kandi X-RAY | lemmer Summary

lemmer is a JavaScript library typically used in Server, Runtime Evironment, Nodejs applications. lemmer has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can install using 'npm i lemmer' or download it from GitHub, npm.

English Lemmer interface for Node.js

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Quality

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Support

lemmer has a low active ecosystem.

It has 12 star(s) with 12 fork(s). There are 4 watchers for this library.

It had no major release in the last 12 months.

There are 1 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 lemmer is 0.3.0

Quality

lemmer has no bugs reported.

Security

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

License

lemmer 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

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

Deployable package is available in npm.

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lemmer Key Features

No Key Features are available at this moment for lemmer.

lemmer Examples and Code Snippets

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Community Discussions

Trending Discussions on lemmer

Vuetify - using change event assign the selected item to a data property

Saving parent item and child items with properties from select element ASP.NET Core MVC

can someone tell whats remove_punct_dict command is doing ?? and whats the output of last line command?

Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words

Lemmatizing whole sentence in python does not work

How to read n number of files in n variables and then add those variables in a list?

Counting matrix pairs using a threshold

QUESTION

Vuetify - using change event assign the selected item to a data property

Asked 2020-Aug-25 at 16:55

I have a v-select that looks like this:

...

ANSWER

Answered 2020-Aug-25 at 15:01

In your case, you cannot use site.name because if you make item-value="id", site value will be the same as id. I made a simple codepen for your issue. Please reference it. https://codepen.io/endmaster0809/pen/OJNpmyG

Source https://stackoverflow.com/questions/63578852

QUESTION

Saving parent item and child items with properties from select element ASP.NET Core MVC

Asked 2020-Aug-25 at 06:27

In ASP.NET Core MVC web app I'm working on, I have a problem that I'll illustrate with the following example.

Let's say the user has to save book with its title and number of pages, and one or more authors. For each author, user should choose his name and role from dropdown list.

Model has four classes:

...

ANSWER

Answered 2020-Aug-25 at 06:27

if I fill the form like this Filled form, with two authors for both elements of list BookAuthors, preoperties AuthorID and AuthorRoleID are null

That is because your partial view contains an input element which is not the property in BookAuthors,but you define it as BookAuthors.Index,the model binding system would misunderstand it:

Source https://stackoverflow.com/questions/63555475

QUESTION

can someone tell whats remove_punct_dict command is doing ?? and whats the output of last line command?

Asked 2020-May-11 at 21:33

def LemTokens(tokens):
    return [lemmer.lemmatize(token) for token in tokens]

remove_punct_dict = dict((ord(punct), None) for punct in string.punctuation)

def LemNormalize(text):
    return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))

...

ANSWER

Answered 2020-May-11 at 21:33

remove_punct_dict is actually a dict collection of the Unicode value of all punctuation found in string.punctuation with value None

Source https://stackoverflow.com/questions/61738974

QUESTION

Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words

Asked 2020-Feb-18 at 20:48

I am making a chatbot using Python. Code:

...

ANSWER

Answered 2020-Feb-18 at 12:15

The reason is that you have used custom tokenizer and used default stop_words='english' so while extracting features a check is made to see if there is any inconsistency between stop_words and tokenizer

If you dig deeper into the code of sklearn/feature_extraction/text.py you will find this snippet performing the consistency check:

Source https://stackoverflow.com/questions/60280307

QUESTION

Lemmatizing whole sentence in python does not work

Asked 2019-Feb-23 at 21:03

I am using WordNetLemmatizer() function in NLTK package in python to lemmatize the entire sentence of movie review dataset.

Here is my code:

...

ANSWER

Answered 2019-Feb-23 at 20:38

You have to pass 'v' (verb) to lemmatize:

Source https://stackoverflow.com/questions/54845821

QUESTION

Map the most similar cosine ranking document back to each respective document in my original list

Asked 2019-Feb-14 at 02:44

I can't figure out how to map the top (#1) most similar document in my list back to each document item in my original list.

I go through some preprocessing, ngrams, lemmatization, and TF IDF. Then I use Scikit's linear kernal. I tried using extract features, but am not sure how to work with it in the csr matrix...

Tried various things (Using csr_matrix of items similarities to get most similar items to item X without having to transform csr_matrix to dense matrix)

...

ANSWER

Answered 2019-Feb-14 at 02:44

import pandas as pd

df = pd.DataFrame(columns=["original df col", "most similar doc", "similarity%"])
for i in range(len(documents)):
    cosine_similarities = linear_kernel(tfidf_matrix[i:i+1], tfidf_matrix).flatten()
    # make pairs of (index, similarity)
    cosine_similarities = list(enumerate(cosine_similarities))
    # delete the cosine similarity with itself
    cosine_similarities.pop(i)
    # get the tuple with max similarity
    most_similar, similarity = max(cosine_similarities, key=lambda t:t[1])
    df.loc[len(df)] = [documents[i], documents[most_similar], similarity]

Source https://stackoverflow.com/questions/54681776

QUESTION

How to read n number of files in n variables and then add those variables in a list?

Asked 2018-Sep-12 at 11:29

This is my code:

...

ANSWER

Answered 2018-Sep-12 at 10:54

You could collect all the in a list, for example: lst = []

Source https://stackoverflow.com/questions/52293426

QUESTION

Counting matrix pairs using a threshold

Asked 2017-Dec-19 at 16:25

I have a folder with hundreds of txt files I need to analyse for similarity. Below is an example of a script I use to run similarity analysis. In the end I get an array or a matrix I can plot etc.

I would like to see how many pairs there are with cos_similarity > 0.5 (or any other threshold I decide to use), removing cos_similarity == 1 when I compare the same files, of course.

Secondly, I need a list of these pairs based on file names.

So the output for the example below would look like:

1

and

["doc1", "doc4"]

Will really appreciate your help as I feel a bit lost not knowing which direction to go.

This is an example of my script to get the matrix:

...

ANSWER

Answered 2017-Dec-19 at 16:25

As I understood your question, you want to create a function that reads the output numpy array and a certain value (threshold) in order to return two things:

how many docs are bigger than or equal the given threshold
the names of these docs.

So, here I've made the following function which takes three arguments:

the output numpy array from cos_similarity() function.
list of document names.
a certain number (threshold).

And here it's:

Source https://stackoverflow.com/questions/47872303

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

Vulnerabilities

No vulnerabilities reported

Install lemmer

You can install using 'npm i lemmer' or download it from GitHub, npm.

Support

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