How to use corpora.dictionary function

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by Abdul Rawoof A R dot icon Updated: Oct 19, 2023

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A corpus is a collection of text or other forms of linguistic data. It is compiled and organized for linguistic analysis, research, or language-related tasks. Corpora is a valuable resource for linguists, researchers, and language professionals. We can use it for a variety of purposes in the field of linguistics and related disciplines.   

Here are some different types of corpora that we can use for research:   

1.General Text Corpora:   

Web Corpora: Collections of text from websites, search engine results, and online forums. Examples include the Common Crawl corpus and Google Books Ngram corpus.   

Newspaper Corpora: Texts from newspapers are often used for studying language change.   

Fiction Corpora: Texts from novels, short stories, and other fictional works. It is useful for literary analysis and stylistic research.   

2. Specialized Corpora:   

Medical Corpora: Collections of medical texts for research in healthcare and biomedicine.   

Legal Corpora: Texts from legal documents, statutes, and court cases for research.   

3. Historical Corpora:   

Diachronic Corpora: Corpora that tracks language changes over time.   

4. Spoken Language Corpora:   

Transcribed Speech Corpora: Spoken language transcribed into text. It is often used for sociolinguistic and phonetic research.   

 Conversational Corpora: Recordings of everyday conversations, telephone calls, or interviews. We can use it for discourse analysis and pragmatics research.   

5. Multilingual Corpora:   

Parallel Corpora: We can align the texts in two or more languages. It will facilitate translation.   

Comparable Corpora: Collect texts in different languages or varieties on similar topics. It is useful for cross-linguistic studies.   

Here is an example of how to use the corpora.dictionary function:   

Fig: Preview the output you will get on running this code from your IDE.


In this solution, we are using the Gensim library.


Follow the steps carefully to get the output easily.

  1. Install PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. Install Gensim - pip install gensim.
  4. Create a new Python file(e.g.:
  5. Copy the snippet using the 'copy' button and paste it into that file(add a print statement at the end of the code).
  6. Run the file using the run button.

I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.

I found this code snippet by searching for 'Problem with creating dictionary with gensim for LDA' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created in PyCharm 2022.3.3.
  2. The solution is tested on Python 3.9.7.
  3. Gensim version 4.3.0.

Using this solution, we are able to use the corpora. dictionary function with simple steps. This process also facilitates an easy-to-use, hassle-free method to create a hands-on working version of code which would help us to use the corpora. dictionary function.

Dependent Library

gensimby RaRe-Technologies

Python doticonstar image 14417 doticonVersion:4.3.0doticon
License: Weak Copyleft (LGPL-2.1)

Topic Modelling for Humans


            gensimby RaRe-Technologies

            Python doticon star image 14417 doticonVersion:4.3.0doticon License: Weak Copyleft (LGPL-2.1)

            Topic Modelling for Humans

                      You can also search for any dependent libraries on kandi like 'Gensim'.


                      1. What is the corpus constructor command? How can we use it to create a speech corpus?   

                      The term "corpus constructor command" is not a standard recognized term in NLP. We can provide information to create a speech corpus and tools involved in the process.   

                      A speech corpus is a collection of recorded spoken language data. We can use it for speech recognition, text-to-speech synthesis, and linguistic research. 


                      2. How does the Natural Language Toolkit incorporate NLP into its corpora structure?   

                      The Natural Language Toolkit (NLTK) is a popular Python library. It works with human language data. It includes textual data. It provides various tools and resources for natural language processing (NLP). NLTK incorporates natural language processing into its corpora structure through its "corpus" module. It offers a collection of text data for various languages and domains.   

                      3. What are language corpora, and what makes them useful for linguistics research?   

                      Language corpora are large collections of text or spoken language data. We can gather and store linguistic analysis and research. These corpora can encompass a wide range of texts. It includes books, newspapers, websites, transcripts of spoken conversations, and more.   

                      4. How does the Linguistic Data Consortium help to compile data in various dictionaries?   

                      Here's how the LDC helps compile data in various dictionaries:   

                      • Data Collection   
                      • Corpus Creation   
                      • Data Annotation   
                      • Multilingual Resources   
                      • Licensing and Distribution   
                      • Collaboration   
                      • Resource Development   
                      • Long-Term Data Preservation   

                      5. What is the Corpus of Contemporary American English? Why might it be advantageous for computational lexicography?  

                      The Corpus of Contemporary American English (COCA) is a comprehensive and large-scale corpus. It is one of the most used corpora in linguistics and computational lexicography. We can maintain it by COCA Brigham Young University. We can design it to represent various written and spoken texts from different genres.   

                      Here are some advantages of COCA for computational lexicography and linguistic research:   

                      • Size and Diversity   
                      • Up-to-Date Information   
                      • Representativeness   
                      • Search and Analysis Tools 


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