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ansj_seg | ansj分词.ict的真正java实现.分词效果速度都超过开源版的ict. 中文分词,人名识别,词性标注,用户自定义词典 | Natural Language Processing library

 by   NLPchina Java Version: 3.7.5 License: Apache-2.0

 by   NLPchina Java Version: 3.7.5 License: Apache-2.0

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kandi X-RAY | ansj_seg Summary

ansj_seg is a Java library typically used in Artificial Intelligence, Natural Language Processing, Bert applications. ansj_seg 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.
ansj分词.ict的真正java实现.分词效果速度都超过开源版的ict. 中文分词,人名识别,词性标注,用户自定义词典
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • ansj_seg has a medium active ecosystem.
  • It has 5973 star(s) with 2286 fork(s). There are 675 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 32 open issues and 662 have been closed. On average issues are closed in 189 days. There are 6 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of ansj_seg is 3.7.5
ansj_seg Support
Best in #Natural Language Processing
Average in #Natural Language Processing
ansj_seg Support
Best in #Natural Language Processing
Average in #Natural Language Processing

quality kandi Quality

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

securitySecurity

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

license License

  • ansj_seg 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.
ansj_seg License
Best in #Natural Language Processing
Average in #Natural Language Processing
ansj_seg License
Best in #Natural Language Processing
Average in #Natural Language Processing

buildReuse

  • ansj_seg releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • ansj_seg saves you 4198 person hours of effort in developing the same functionality from scratch.
  • It has 8909 lines of code, 549 functions and 137 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
ansj_seg Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
ansj_seg Reuse
Best in #Natural Language Processing
Average in #Natural Language Processing
Top functions reviewed by kandi - BETA

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

  • Generate a summary .
  • Main step of the grammar .
  • Create a tokenizer .
  • Gets viterbi .
  • Analyze the analysis .
  • load feature weight
  • Read a string from the stream
  • Alert for word alert
  • Validate token .
  • Gets varybi .

ansj_seg Key Features

补充文档,增加调用实例和说明

增加一些规则性Recognition,举例[身份证号码识别](https://github.com/NLPchina/ansj_seg/blob/master/src/main/java/org/ansj/recognition/impl/IDCardRecognition.java),目前未完成的有 时间识别,IP地址识别,邮箱识别,网址识别,`词性识别`等…​

提供更加优化的CRF模型。替换ansj的默认模型。

补充测试用例,n多地方测试不完全。如果你有兴趣可以帮忙啦!

重构人名识别模型。增加机构名识别等模型。

增加句法文法分析

实现lstm的分词方式

拾遗补漏…​

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 ansj_seg

You can download it from GitHub.
You can use ansj_seg 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 ansj_seg 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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