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CLAVIN-NERD | Stanford NLP Implementation of the CLAVIN | Natural Language Processing library

 by   Novetta Java Version: v3.0.0 License: GPL-3.0

 by   Novetta Java Version: v3.0.0 License: GPL-3.0

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kandi X-RAY | CLAVIN-NERD Summary

CLAVIN-NERD is a Java library typically used in Artificial Intelligence, Natural Language Processing applications. CLAVIN-NERD has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub, Maven.
Stanford NLP Implementation of the CLAVIN LocationTagger
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • CLAVIN-NERD has a low active ecosystem.
  • It has 21 star(s) with 13 fork(s). There are 14 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 0 open issues and 7 have been closed. On average issues are closed in 174 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of CLAVIN-NERD is v3.0.0
CLAVIN-NERD Support
Best in #Natural Language Processing
Average in #Natural Language Processing
CLAVIN-NERD Support
Best in #Natural Language Processing
Average in #Natural Language Processing

quality kandi Quality

  • CLAVIN-NERD has 0 bugs and 7 code smells.
CLAVIN-NERD Quality
Best in #Natural Language Processing
Average in #Natural Language Processing
CLAVIN-NERD Quality
Best in #Natural Language Processing
Average in #Natural Language Processing

securitySecurity

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

license License

  • CLAVIN-NERD is licensed under the GPL-3.0 License. This license is Strong Copyleft.
  • Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.
CLAVIN-NERD License
Best in #Natural Language Processing
Average in #Natural Language Processing
CLAVIN-NERD License
Best in #Natural Language Processing
Average in #Natural Language Processing

buildReuse

  • CLAVIN-NERD releases are available to install and integrate.
  • Deployable package is available in Maven.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • CLAVIN-NERD saves you 206 person hours of effort in developing the same functionality from scratch.
  • It has 579 lines of code, 10 functions and 5 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
CLAVIN-NERD Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
CLAVIN-NERD Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
Top functions reviewed by kandi - BETA

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

  • Starts the Stanford NER extraction .
    • Gets the uppercase article .
      • Gets the CLAVIN .
        • Converts an NGIN to a CLAVIN
          • Main entry point .
            • Extract location names from the given text .

              Get all kandi verified functions for this library.

              Get all kandi verified functions for this library.

              CLAVIN-NERD Key Features

              Stanford NLP Implementation of the CLAVIN LocationTagger

              Community Discussions

              Trending Discussions on Natural Language Processing
              • number of matches for keywords in specified categories
              • Apple's Natural Language API returns unexpected results
              • Tokenize text but keep compund hyphenated words together
              • Create new boolean fields based on specific bigrams appearing in a tokenized pandas dataframe
              • 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 CLAVIN-NERD

              CLAVIN-NERD relies on CLAVIN to build its lucene index. You can refer to the instructions for getting started with CLAVIN before attempting to work with CLAVIN-NERD. Here are the instructions for building the index using CLAVIN-NERD:.
              Check out a copy of the source code:
              Move into the newly-created CLAVIN-NERD directory:
              Download the latest version of allCountries.zip gazetteer file from GeoNames.org:
              Unzip the GeoNames gazetteer file:
              Package the source code:
              Create the Lucene Index (this one-time process will take several minutes):
              Run the example program:

              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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