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Have you ever “talked” to Siri? Used a spell-checker? Taken advantage of Google Translate in your foreign language class? If so, you’ve experienced the power of natural language processing.
What is natural language processing?
Natural language processing (often shortened to “NLP”) is a subfield of artificial intelligence where software is taught to interpret or replicate human language. Some of the goals of NLP include:
- Determining the meanings of words and phrases in the context of, say, an article
- Transcribing spoken words into writing
- Extracting the themes, moods, or other attributes of a piece of writing
These goals can be approached in a number of ways—for example, by using machine learning algorithms.
What are some applications of NLP?
For those with access to the technologies mentioned at the beginning of this article, NLP has been revolutionary, reshaping their everyday lives by helping them carry out their everyday errands more conveniently and efficiently. But NLP has also been the subject of promising research at the forefront of the AI field, leading to its application to a variety of tasks. Here are a few examples:
- NLP for content moderation: Many companies are already using NLP to monitor and regulate content published to their digital platforms—for example, online forums—in an effort to reduce violent or hateful speech.
- NLP for political analysis: NLP can be used to parse and evaluate large databases of political texts (including Tweets!) in order to determine trends—for example, voter behavior.
- NLP for research paper generation: A team at MIT developed a program called “SCIgen” which, using NLP, writes “random CS research papers”.
What are some limitations of NLP?
As with any other AI innovation, NLP has its limitations, along with a fair share of ethical concerns:
- NLP can perpetuate pre-existing biases. Last summer, I attended a talk by Ayanna Howard, an accomplished professor and roboticist. During the talk, Dr. Howard described the struggles her team faced in building a robot capable of travelling over snowy terrain. These struggles stemmed from a simple problem: no one on their team had much experience with snow! Dr. Howard’s anecdote illustrates an important point: when technology (including NLP software) is developed by a “biased” group of technologists, it can perpetuate those biases. NLP can reflect the biases in language pervasive in NLP datasets (for example, the association of certain adjectives to certain groups of people).
- NLP for content moderation can be ineffective or lead to censorship. Our current NLP cannot grasp many of the nuances of human language—for example, sarcasm. And some worry NLP may ban posts which would actually be protected under free speech, while missing posts crafted with truly malicious intent.
I want to get involved with NLP. What do I do?
There are a number of ways to get involved! Here are some suggestions:
- NLP-related projects: If you have the means, reach out to local professors to ask about conducting NLP research under their supervision! You can probably find a list of ongoing projects on a professor’s website; look into those that interest you. If you’re unable to pursue NLP research, consider looking into online projects, including open-source projects, that you may be able to contribute to.
- NLP-related courses: Head over to our ‘Resources’ page for a list of over 150 links to various digital learning websites! Many of these websites offer free and open NLP courses. Or, head to your local library to check out an NLP book!
Hope this article helped you learn something new about NLP, and maybe even gave you an idea for your next project. Happy coding!