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nlp-journey | Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificat | Natural Language Processing library

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kandi X-RAY | nlp-journey Summary

nlp-journey is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Natural Language Processing, Bert applications. nlp-journey has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.
Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation),etc. All codes are implemented intensorflow 2.0.

kandi-support Support

  • nlp-journey has a medium active ecosystem.
  • It has 1320 star(s) with 356 fork(s). There are 61 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 0 open issues and 5 have been closed. On average issues are closed in 29 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of nlp-journey is v1.0

quality kandi Quality

  • nlp-journey has 0 bugs and 0 code smells.

securitySecurity

  • nlp-journey has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • nlp-journey code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.

license License

  • nlp-journey is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.

buildReuse

  • nlp-journey releases are available to install and integrate.
  • Build file is available. You can build the component from source.
Top functions reviewed by kandi - BETA

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

  • Performs step 4 steps .
  • Run the example
  • Transform a sentence using jieba .
  • load training data
  • Build prediction for given sentence .
  • Clean text .
  • Compute loss function .
  • Calculate dot product attention .
  • Load data from scratch .
  • Evaluate the model .

nlp-journey Key Features

Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation),etc. All codes are implemented intensorflow 2.0.

nlp-journey Examples and Code Snippets

No Code Snippets are available at this moment for nlp-journey.Refer to component home page for details.

No Code Snippets are available at this moment for nlp-journey.Refer to component home page for details.

Community Discussions

Trending Discussions on Natural Language Processing
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  • Assigning True/False if a token is present in a data-frame
  • How to calculate perplexity of a sentence using huggingface masked language models?
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Trending Discussions on Natural Language Processing

QUESTION

number of matches for keywords in specified categories

Asked 2022-Apr-14 at 13:32

For a large scale text analysis problem, I have a data frame containing words that fall into different categories, and a data frame containing a column with strings and (empty) counting columns for each category. I now want to take each individual string, check which of the defined words appear, and count them within the appropriate category.

As a simplified example, given the two data frames below, i want to count how many of each animal type appear in the text cell.

df_texts <- tibble(
  text=c("the ape and the fox", "the tortoise and the hare", "the owl and the the 
  grasshopper"),
  mammals=NA,
  reptiles=NA,
  birds=NA,
  insects=NA
)

df_animals <- tibble(animals=c("ape", "fox", "tortoise", "hare", "owl", "grasshopper"),
           type=c("mammal", "mammal", "reptile", "mammal", "bird", "insect"))

So my desired result would be:

df_result <- tibble(
  text=c("the ape and the fox", "the tortoise and the hare", "the owl and the the 
  grasshopper"),
  mammals=c(2,1,0),
  reptiles=c(0,1,0),
  birds=c(0,0,1),
  insects=c(0,0,1)
)

Is there a straightforward way to achieve this keyword-matching-and-counting that would be applicable to a much larger dataset?

Thanks in advance!

ANSWER

Answered 2022-Apr-14 at 13:32

Here's a way do to it in the tidyverse. First look at whether strings in df_texts$text contain animals, then count them and sum by text and type.

library(tidyverse)

cbind(df_texts[, 1], sapply(df_animals$animals, grepl, df_texts$text)) %>% 
  pivot_longer(-text, names_to = "animals") %>% 
  left_join(df_animals) %>% 
  group_by(text, type) %>% 
  summarise(sum = sum(value)) %>% 
  pivot_wider(id_cols = text, names_from = type, values_from = sum)

  text                                   bird insect mammal reptile
  <chr>                                 <int>  <int>  <int>   <int>
1 "the ape and the fox"                     0      0      2       0
2 "the owl and the the \n  grasshopper"     1      0      0       0
3 "the tortoise and the hare"               0      0      1       1

To account for the several occurrences per text:

cbind(df_texts[, 1], t(sapply(df_texts$text, str_count, df_animals$animals, USE.NAMES = F))) %>% 
  setNames(c("text", df_animals$animals)) %>% 
  pivot_longer(-text, names_to = "animals") %>% 
  left_join(df_animals) %>% 
  group_by(text, type) %>% 
  summarise(sum = sum(value)) %>% 
  pivot_wider(id_cols = text, names_from = type, values_from = sum)

Source https://stackoverflow.com/questions/71871613

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

Vulnerabilities

No vulnerabilities reported

Install nlp-journey

You can download it from GitHub.
You can use nlp-journey 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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