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dependency-parsing-toolbox | Dependency Parsing toolbox | Natural Language Processing library

 by   mojtaba-khallash Java Version: Current License: No License

 by   mojtaba-khallash Java Version: Current License: No License

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kandi X-RAY | dependency-parsing-toolbox Summary

dependency-parsing-toolbox is a Java library typically used in Institutions, Learning, Education, Artificial Intelligence, Natural Language Processing applications. dependency-parsing-toolbox has no bugs, it has no vulnerabilities and it has low support. However dependency-parsing-toolbox build file is not available. You can download it from GitHub.
In the name of Allah.
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kandi-support Support

  • dependency-parsing-toolbox has a low active ecosystem.
  • It has 19 star(s) with 4 fork(s). There are 3 watchers for this library.
  • It had no major release in the last 12 months.
  • dependency-parsing-toolbox has no issues reported. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of dependency-parsing-toolbox is current.
This Library - Support
Best in #Natural Language Processing
Average in #Natural Language Processing
This Library - Support
Best in #Natural Language Processing
Average in #Natural Language Processing

quality kandi Quality

  • dependency-parsing-toolbox has 0 bugs and 0 code smells.
This Library - Quality
Best in #Natural Language Processing
Average in #Natural Language Processing
This Library - Quality
Best in #Natural Language Processing
Average in #Natural Language Processing

securitySecurity

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

license License

  • dependency-parsing-toolbox 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.
This Library - License
Best in #Natural Language Processing
Average in #Natural Language Processing
This Library - License
Best in #Natural Language Processing
Average in #Natural Language Processing

buildReuse

  • dependency-parsing-toolbox releases are not available. You will need to build from source code and install.
  • dependency-parsing-toolbox has no build file. You will be need to create the build yourself to build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • dependency-parsing-toolbox saves you 119220 person hours of effort in developing the same functionality from scratch.
  • It has 126343 lines of code, 7104 functions and 926 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
This Library - Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
This Library - Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
Top functions reviewed by kandi - BETA

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

  • Generate a CEL - 07 string for compilation .
    • Runs the postag -ag algorithm .
      • SiblingM .
        • Find the first and last m for an instance .
          • Add features .
            • Add core features .
              • Add FCF information .
                • Extracts all features of a parse .
                  • Reads a sentence .
                    • Uses the Levenshtein algorithm to compute the best and the best Edges .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      dependency-parsing-toolbox Key Features

                      "Dependency Parsing toolbox" integrates different algorithms related to dependency parsing in one place. This toolbox has been developed by Mojtaba Khallash from Iran University of Science and Technology (IUST).

                      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 dependency-parsing-toolbox

                      You can download it from GitHub.
                      You can use dependency-parsing-toolbox 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 dependency-parsing-toolbox 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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