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Processing unstructured text can take various approaches. One way is to split paragraphs based on new lines and break sentences by focusing on spaces. However, this can lead to the need for sourcing your own scoring data to join with your unstructured data source, and that requires data warehousing services internally or externally. Finding, cleaning, and processing your word scoring data sources is a project that becomes a big part of this realm of solving. NLP can be considered a row-level relationship when using a relational database solution, but NLTK provides a Python alternative that eliminates the need for processing data differently than what a relational database would solve. An acid compliant database would prioritize establishing a relationship with “word scoring” tables, requiring scored data per word, which can be a time-consuming task. Instead, there are more precise and efficient methods available, such as those showcased in this blog post, which include resources like YouTube content, Python code, Code walkthroughs, and a cloud version of Jupyter notebook code that your digital marketing team can use to start solving problems immediately.

Both row level scoring per word and NLP are both power tools when trying to understand data. Data engineering services will open a new level of data solution development, and allow you to quickly harness different levels of capabilities with your internal and external data sources.

Natural Language Processing, or NLP for short, is a branch of artificial intelligence that deals with the interaction between computers and human languages. It is a field that has seen tremendous growth in recent years, with applications ranging from language translation to sentiment analysis, and even to building intelligent virtual assistants.

At its core, NLP is about teaching computers to understand and process human language. This is a challenging task, as human language is highly ambiguous and context-dependent. For example, the word “bass” can refer to a type of fish or a low-frequency sound, and the word “bat” can refer to an animal or a piece of sports equipment. Understanding the intended meaning in a given context requires a deep understanding of the language and the context in which it is used.

There are several key techniques that are used in NLP, including:

  • Tokenization: This is the process of breaking down a sentence or text into individual words or phrases. This is the first step in any NLP process, as it allows the computer to work with the individual elements of the text.
  • Part-of-speech tagging: This is the process of identifying the role of each word in a sentence, such as whether it is a noun, verb, adjective, etc. This helps the computer understand the grammar and structure of the sentence.
  • Named Entity Recognition: This is the process of identifying proper nouns and entities in a sentence such as people, places, and organizations. This can be used to extract structured information from unstructured text.
  • Sentiment Analysis: This is the process of determining the emotional tone of a piece of text. This can be used to understand how people feel about a particular topic or product.
  • Machine Translation: This is the process of converting text from one language to another. This can be used to translate documents, websites or even speech.

These are just a few examples of the many techniques used in NLP. The field is constantly evolving, with new techniques and algorithms being developed all the time. As the amount of text data available on the internet continues to grow, the importance of NLP will only increase. It is a fascinating field that has the potential to revolutionize how we interact with technology, and understanding the basics of NLP is essential for anyone working in technology or data science.

In conclusion, NLP is a rapidly growing field that deals with teaching computers to understand human languages. It encompasses a wide range of techniques and applications, from tokenization and part-of-speech tagging to sentiment analysis and machine translation. With the increasing amount of text data available, understanding the basics of NLP is essential for anyone working in technology or data science.

10 Blog Resources related to NLP;

  1. The Stanford Natural Language Processing Group:
  2. Google AI Blog:
  3. Hugging Face:
  4. SpaCy:
  5. NLP News:
  6. OpenAI:
  7. KDNuggets:
  8. NLP Progress:
  9. NLP Overview:
  10. The NLP Newsletter: