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MinorThird | Java classes for storing text | Natural Language Processing library

 by   TeamCohen Java Version: Current License: Non-SPDX

 by   TeamCohen Java Version: Current License: Non-SPDX

kandi X-RAY | MinorThird Summary

MinorThird is a Java library typically used in Artificial Intelligence, Natural Language Processing applications. MinorThird has no bugs, it has no vulnerabilities and it has low support. However MinorThird build file is not available and it has a Non-SPDX License. You can download it from GitHub.
MinorThird is a collection of Java classes for storing text, annotating text, and learning to extract entities and categorize text.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • MinorThird has a low active ecosystem.
  • It has 56 star(s) with 17 fork(s). There are 17 watchers for this library.
  • It had no major release in the last 6 months.
  • There are 0 open issues and 2 have been closed. On average issues are closed in 0 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of MinorThird is current.
MinorThird Support
Best in #Natural Language Processing
Average in #Natural Language Processing
MinorThird Support
Best in #Natural Language Processing
Average in #Natural Language Processing

quality kandi Quality

  • MinorThird has no bugs reported.
MinorThird Quality
Best in #Natural Language Processing
Average in #Natural Language Processing
MinorThird Quality
Best in #Natural Language Processing
Average in #Natural Language Processing

securitySecurity

  • MinorThird has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
MinorThird Security
Best in #Natural Language Processing
Average in #Natural Language Processing
MinorThird Security
Best in #Natural Language Processing
Average in #Natural Language Processing

license License

  • MinorThird has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
MinorThird License
Best in #Natural Language Processing
Average in #Natural Language Processing
MinorThird License
Best in #Natural Language Processing
Average in #Natural Language Processing

buildReuse

  • MinorThird releases are not available. You will need to build from source code and install.
  • MinorThird has no build file. You will be need to create the build yourself to build the component from source.
MinorThird Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
MinorThird Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
Top functions reviewed by kandi - BETA

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

  • Runs the pass on the specified type
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs through the method invocation
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the break statement
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs this pass on the specified type
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the expression
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the given list of nodes
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the given instance
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Processes a continue statement
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the given type
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on each node
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on all nodes
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on each try statement
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs a pass on the headFinder
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs this pass on all nodes of the specified type
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs this pass on each node
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the specified instance
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass through
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the AST
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the given node
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the binary expression
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the given assignment
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the object
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the switch statement
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the exception
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the passed in through statement
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the constraint
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs through the specified statement
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the pass on the binary constraint
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Process all nodes of the given type
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner
  • Runs the code for the given instruction
    • Generate code for a bounded constraint
    • Generates the codes that should be used for the classification
    • Generate the code required for this learner

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Get all kandi verified functions for this library.

MinorThird Key Features

MinorThird is a collection of Java classes for storing text, annotating text, and learning to extract entities and categorize text.

MinorThird Examples and Code Snippets

Community Discussions

Trending Discussions on Natural Language Processing
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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 MinorThird

You can download it from GitHub.
You can use MinorThird 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 MinorThird 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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Clone
  • https://github.com/TeamCohen/MinorThird.git

  • gh repo clone TeamCohen/MinorThird

  • git@github.com:TeamCohen/MinorThird.git

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