Natural Language Processing (NLP) is a subfield of computer science and artificial intelligence that focuses on enabling computers to understand and process human language [1].

How does it work, and how can you apply it to your needs?

Let’s figure it out.

How does Natural Language Processing (NLP) work?

NLP involves algorithms and statistical models to analyze, understand, and generate natural language text. NLP is an interdisciplinary field that draws from linguistics, computer science, and artificial intelligence, making it a crucial aspect of developing AI technologies [2].

The history of NLP can be traced back to the 1950s when researchers first began exploring the idea of using computers to process human language.

Over the years, NLP has evolved, and advancements in machine learning have made it possible to develop sophisticated NLP models that can process large amounts of text data in real-time.

NLP Techniques

To understand NLP, knowing about the various NLP techniques is essential.

Some of the most common NLP techniques include:

• Tokenization: This is the process of splitting a sentence or text into smaller units called tokens. Tokens can be words, phrases, or symbols.

• Named Entity Recognition: This is the process of identifying and categorizing entities in a text, such as people, places, and organizations.

• Part-of-Speech Tagging: This is the process of identifying and labeling the parts of speech in a sentence, such as nouns, verbs, adjectives, etc. Sentiment Analysis: This is the process of determining the sentiment expressed in a text, such as positive, negative, or neutral [3].

• Text Classification: This is the process of categorizing a text into predefined categories based on its content [4].

NLP Tools and Libraries

There are many NLP tools and libraries available that make it easier to develop NLP applications. Some of the most popular NLP tools and libraries include NLTK, spaCy, CoreNLP, and Gensim. These tools provide a range of NLP functionality, from basic text processing to advanced language models.

When choosing an NLP tool or library, it’s essential to consider factors such as the functionality required, performance, ease of use, and community support.

For example, some NLP tools are designed for specific use cases, such as sentiment analysis or text classification, while others offer a more comprehensive range of NLP functionality [5].

Ok, but where can you apply it?

There’s more than one area worth your attention.

Where can you apply the NLP?

NLP is used in many different applications, including chatbots and conversational AI, sentiment analysis, text summarization, e-commerce search, information extraction, language translation, and language models.

Let’s discuss each application.

• E-commerce search: NLP is an irreplaceable tool for achieving a highly user-relevant e-commerce search experience. NLP in e-commerce search allows users to search the way they speak (using jargon, abbreviations, slang, or writing spelling mistakes). Such language recognition helps e-shops to offer a seamless shopping experience for their shoppers.

• Chatbots and GenAI: NLP is used to process and understand user inputs and provide relevant responses. NLP techniques such as named entity recognition and sentiment analysis are used to help chatbots understand the context of the conversation and provide appropriate responses [6].

For instance, Lupa GenAI (a chatbot, specifically designed to serve the needs of e-commerce search users) offers a conversation-like search experience that drastically increases e-commerce sales.

• Sentiment Analysis: This enables organizations to gather valuable insights into customer sentiment towards their products or services. NLP models are trained on large datasets to identify the sentiment expressed in customer feedback, such as online reviews or social media posts [7].

• Text Summarization: This technique automatically generates a condensed text version that retains essential information. NLP algorithms analyze the text, identify the most important sentences, and generate a summary that is shorter but still conveys the essential information [8].

• Information Extraction: This is used to extract specific information from large amounts of unstructured text data. NLP algorithms can identify patterns in text data and extract information such as names, addresses, and dates [9].

• Language Translation: NLP models can translate text from one language to another, making it possible for people to communicate with each other even if they don’t speak the same language [10].

The same applies to e-commerce search. For instance, LupaSearch supports multiple languages. That means Polish users can search in their native language (even if the e-shop primarily uses the English language).

Challenges and Limitations of NLP

Despite its many benefits, NLP also has its challenges and limitations:

• Ambiguity and Diversity of Natural Language: Human language is complex and often contains multiple meanings, making it difficult for NLP algorithms to accurately interpret and process text data [11].

• Performance of Current Models: While NLP models have improved significantly in recent years, they still have limitations, particularly in handling complex language structures and idiomatic expressions [12].

• Developing NLP Applications: This can be challenging, particularly for organizations that don’t have the necessary technical expertise or resources. NLP models require large amounts of training data, which can be difficult and time-consuming to obtain [13].

Future of NLP

In conclusion, NLP is an important and rapidly growing field transforming our interactions with computers and information access. NLP techniques such as tokenization, named entity recognition, sentiment analysis, and text classification have many applications. [14].

However, NLP also has its challenges and limitations, including the complexity of human language, the performance of current NLP models, and the difficulties of developing NLP applications [15].

Despite these challenges, the future of NLP looks bright. Advances in machine learning and artificial intelligence will continue to improve the performance of NLP models, and new applications of NLP will emerge as the technology continues to evolve [16].

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References

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3. Omar, M., Choi, S., Nyang, D., & Mohaisen, D. (2022). Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions. IEEE Access, 10, 86038-86056. https://doi.org/10.1109/ACCESS.2022.3197769.

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8. Surabhi, M. (2013). Natural language processing future. 2013 International Conference on Optical Imaging Sensor and Security (ICOSS), 1-3. https://doi.org/10.1109/ICOISS.2013.6678407.

9. Araujo, L. (2019). Genetic programming for natural language processing. Genetic Programming and Evolvable Machines, 21, 11 - 32. https://doi.org/10.1007/s10710-019-09361-5.

10. Farghaly, A., & Shaalan, K. (2009). Arabic Natural Language Processing: Challenges and Solutions. ACM Trans. Asian Lang. Inf. Process., 8, 14:1-14:22. https://doi.org/10.1145/1644879.1644881.

11. Krasadakis, P., Sakkopoulos, E., & Verykios, V. (2021). A Natural Language Processing Survey on Legislative and Greek Documents. Proceedings of the 25th Pan-Hellenic Conference on Informatics. https://doi.org/10.1145/3503823.3503898.

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14. Yang, L., Ng, W., Foo, L., Liu, Y., Yan, M., Lei, X., Zhang, X., & Ting, D. (2021). Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions. Current Opinion in Ophthalmology, 32, 397 - 405. https://doi.org/10.1097/ICU.0000000000000789.

15. Wen, B. (2023). Research on the applications of natural language processing. Applied and Computational Engineering. https://doi.org/10.54254/2755-2721/16/20230896.

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