stopwords | Multilingual Stopword Lists in R | Natural Language Processing library

One way you could do this is split your word vector into N sections and set each as a column in a data frame. Then just set the column names to be empty except for the first. In below example I've done this manually but the process should be relatively simple to automate if you don't know in advance how long the vector will be.

Shouldn't it be:

Your words statement finds the words that you care about (removing stopwords) in the text of the whole column. We can change that a bit to apply the replacement on each row instead:

Create a sentence index

In terms of limiting a single thread from using 100% CPU, there are architectural ways of doing so at a server level, however I don't think that's really the outcome you would want. A CPU using 100% isn't an issue (CPUs will often spike to 100% CPU for very short periods of time to process things as quickly as possible), it's more of it using 100% CPU for an extended period of time and preventing other applications from getting CPU cycles.

From what I am seeing in the example code, it might be a better solution to use Queues within NestJS. Documentation can be seen here using Bull. This way you can utilize the rate limits of jobs being processed and tweak it there, and other applications will not be waiting for the completion of the entire process.

For instance if you have 100,000 files to process, you may want to create a job that will process 1,000 of them at a time and create 100 jobs to be thrown into the queue. This is a fairly typical process for processes that require a large amount of compute time.

I know this isn't the exactly the answer I am sure you were looking for, but hopefully it will help and provide a solution that is not specific to your architecture.

You need to define a function. We start by adding a space before and after the line. We then loop through the words from stopwords with replace with space. We keep running each replace until the length after the remove is the same as before it. Finally we use TRIM to remove the spaces before and after the string.

Your question is a combination of text mining tasks, which I try to briefly address here. The first step is, as always in NLP and text mining projects, the cleaning one, including removing stop words, stop characters, etc.:

Try this:

Here you are using the StandardTokenizerFactory for your field which is creating a token as shirt and hence a match.

StandardTokenizerFactory :- It tokenizes on whitespace, as well as strips characters

The Documentation for StandardTokenizerFactory mentions as :-

Splits words at punctuation characters, removing punctuations. However, a dot that's not followed by whitespace is considered part of a token. Splits words at hyphens, unless there's a number in the token. In that case, the whole token is interpreted as a product number and is not split. Recognizes email addresses and Internet hostnames as one token.

If you want to perform search on the "t-shirt", then it should be tokenized. I would suggest you to use the KeywordTokenizerFactory

Keyword Tokenizer does not split the input provided to it. It does not do any processing on the string, and the entire string is treated as a single token. This doesn't actually do any tokenization. It returns the original text as one term.

This KeywordTokenizerFactory is used for sorting or faceting requirements, where one want to perform the exact match. Its helpful in faceting and sorting.

You can have another field and apply KeywordTokenizerFactory to it and perform your search on it.