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Applications of OpenOdia tools

Objectives

  • Application of the tools in the package are mentioned here.
  • These will expand your ideas to have practical realistic implementation of NLP tools in Odia language.
  • Which in turn will increase productivity and digital presence of Odia resources.

Generate Odia names

from openodia import name
name.generate_names()
'''
['ଯଦୁମଣୀ ମାଢ଼ୀ', 'ବାସନ୍ତି ବ୍ରହ୍ମା', 'ପ୍ରବୀଣ ସିଂହ ମୁକ୍କିମ', 'ବୃନ୍ଦାବନ ଧଳ', 'ଅଶ୍ୱିନୀ କିଶୋର ଜଗଦେବ', 
 'ଶ୍ରୀଯୁକ୍ତ ଇରାଶିଷ ସେଠୀ', 'କୁମାରୀ ସୁମନ ସିଂଦେଓ', 'ସଲିଲ ଅଲ୍ଲୀ ଛତ୍ରିଆ', 'ଦିବାକରନାଥ ରାଧାରାଣୀ ଆଚାର୍ଯ୍ୟ', 'ଦୁର୍ଗା ସୁନ୍ଦରସୁର୍ଯ୍ୟା ପୁଟୀ']
'''
Name morphology
  • In Odia names, mostly the surname carries the family name.
  • Therefore, there is a limited number of surnames, which can be used to identify names in a text.
Exceptions
  • There has been a trend recently where, after marriage the spouse add their in-law's surname; which makes two surnames in a single name.
  • However, recently there has been no surnames in the names or surname without any family name has been kept. Which needs to be kept an eye for.

Data Masking

  • As these names database has been taken from Wikipedia and actual person names in Odisha, you can use these as NER for person name recognition.
  • In data engineering projects when you need to ingest large amount of data, you need to mask or anonymize the PI (Personal Identification) data from the data source.
  • This is to remove bias and increase privacy on your data/model.

Masking names:

  • To avoid disrupting privacy of individuals, you can identify the names of people in the text and
  • Replace the names with a mask tag like <name>.
  • Example
    • ବୈଦ୍ୟନାଥ ସିଂହଦେଓ ବାବୁ ସସ୍ତ୍ରୀକ ନିଜ କାରରେ ବସି ପୁରୀ ବୁଲିଯାଇଛନ୍ତି । has potential personal data that which a person can derive like:
      1. ବୈଦ୍ୟନାଥ ସିଂହଦେଓ is married.
      2. ବୈଦ୍ୟନାଥ ସିଂହଦେଓ is male.
      3. He has a car.
      4. He is in Puri.
    • To avoid this kind of personal privacy exposure, we need to mask the text to <name> ବାବୁ ସସ୍ତ୍ରୀକ ନିଜ କାରରେ ବସି ପୁରୀ ବୁଲିଯାଇଛନ୍ତି ।
  • The problem with this method is you miss the credibility of an important feature of the input data. As there is no uniqueness among the name field.

Anonymization of names:

  • Using this method rather than completely removing all names with a <name> tag you can substitute with a fake name.
  • It maintains the uniqueness of the name field.
  • However, in many cases you may need to revert back to the original person name after a certain operation has been completed. That's not possible as anonymization is an irreversible procedure.
  • Example
    • ବୈଦ୍ୟନାଥ ସିଂହଦେଓ ବାବୁ ସସ୍ତ୍ରୀକ ନିଜ କାରରେ ବସି ପୁରୀ ବୁଲିଯାଇଛନ୍ତି । will be anonymized as ବ୍ରଜମୋହନ ପଣ୍ଡା ବାବୁ ସସ୍ତ୍ରୀକ ନିଜ କାରରେ ବସି ପୁରୀ ବୁଲିଯାଇଛନ୍ତି ।
    • Did you notice how ବୈଦ୍ୟନାଥ ସିଂହଦେଓ has changed to ବ୍ରଜମୋହନ ପଣ୍ଡା?
  • Here are some situations in which you may want to use anonymization:1
    • If you no longer need to communicate or work with a consumer, but wish to archive their activity, order history, or any other details that could not be used to identify them.
    • To perform data analyses that are unrelated to the services you provide the consumer.
    • If you need to make data available to a group of people outside those that are designated to fulfill your services, such as a wide group of employees or consultants.

Pseudonimization of names:

  • This is same as anonymization, with only difference that, the anonymized names can be replaced with their original names.
  • Example
    • ବୈଦ୍ୟନାଥ ସିଂହଦେଓ ବାବୁ ସସ୍ତ୍ରୀକ ନିଜ କାରରେ ବସି ପୁରୀ ବୁଲିଯାଇଛନ୍ତି । will be pseudonymized as hrtvd6wqbvs4320 ବାବୁ ସସ୍ତ୍ରୀକ ନିଜ କାରରେ ବସି ପୁରୀ ବୁଲିଯାଇଛନ୍ତି ।
    • Did you notice how ବୈଦ୍ୟନାଥ ସିଂହଦେଓ has changed to hrtvd6wqbvs4320?
    • Here hrtvd6wqbvs4320 has shown as a demonstration of a key, which can be stored in a hash table with value as ବୈଦ୍ୟନାଥ ସିଂହଦେଓ.
    • This text can be further reversed by referring to this hash table or a unique private key.
  • Data pseudonymization can be used when you will need to re-identify users in the future:1
    • To keep data secure during the fulfillment of services, by masking identifying details to employees or other data handlers that do not need those details.
    • To maintain data protection within your database or records, order histories, and inactive customers that you remain in contact with.
    • To transfer data over international borders.
    • To maintain data protection and Privacy by Design principles laid out by the GDPR.

If you find this page useful, please cite this using:

@misc{OpenOdia,
    author       = {Soumendra Kumar Sahoo},
    title        = {OpenOdia Applications},
    howpublished = {\url{https://www.openodia.soumendrak.com/}},
    year         = {2021}
}

  1. https://www.termsfeed.com/blog/gdpr-pseudonymization-anonymization/ ↩↩

Last update: 2023-03-29
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