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corenlp-examples | Stanford Core NLP API usage examples | Natural Language Processing library

 by   drewfarris Java Version: Current License: No License

 by   drewfarris Java Version: Current License: No License

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kandi X-RAY | corenlp-examples Summary

corenlp-examples is a Java library typically used in Artificial Intelligence, Natural Language Processing applications. corenlp-examples has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
Stanford Core NLP API usage examples.
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kandi-support Support

  • corenlp-examples has a low active ecosystem.
  • It has 27 star(s) with 27 fork(s). There are 3 watchers for this library.
  • It had no major release in the last 12 months.
  • corenlp-examples has no issues reported. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of corenlp-examples is current.
corenlp-examples Support
Best in #Natural Language Processing
Average in #Natural Language Processing
corenlp-examples Support
Best in #Natural Language Processing
Average in #Natural Language Processing

quality kandi Quality

  • corenlp-examples has 0 bugs and 0 code smells.
corenlp-examples Quality
Best in #Natural Language Processing
Average in #Natural Language Processing
corenlp-examples Quality
Best in #Natural Language Processing
Average in #Natural Language Processing

securitySecurity

  • corenlp-examples has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • corenlp-examples code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
corenlp-examples Security
Best in #Natural Language Processing
Average in #Natural Language Processing
corenlp-examples Security
Best in #Natural Language Processing
Average in #Natural Language Processing

license License

  • corenlp-examples 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.
corenlp-examples License
Best in #Natural Language Processing
Average in #Natural Language Processing
corenlp-examples License
Best in #Natural Language Processing
Average in #Natural Language Processing

buildReuse

  • corenlp-examples 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.
  • corenlp-examples saves you 65 person hours of effort in developing the same functionality from scratch.
  • It has 176 lines of code, 2 functions and 3 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
corenlp-examples Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
corenlp-examples Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
Top functions reviewed by kandi - BETA

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

  • Main method for testing .

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    corenlp-examples Key Features

    Stanford Core NLP API usage examples

    corenlp-examples Examples and Code Snippets

    No Code Snippets are available at this moment for corenlp-examples.

    See all Code Snippets related to Natural Language Processing

    Community Discussions

    Trending Discussions on Natural Language Processing
    • number of matches for keywords in specified categories
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    • ModuleNotFoundError: No module named 'milvus'
    • Which model/technique to use for specific sentence extraction?
    • Assigning True/False if a token is present in a data-frame
    • How to calculate perplexity of a sentence using huggingface masked language models?
    • Mapping values from a dictionary's list to a string in Python
    • What are differences between AutoModelForSequenceClassification vs AutoModel
    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 corenlp-examples

    You can download it from GitHub.
    You can use corenlp-examples 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 corenlp-examples 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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