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The field of natural language processing and the many topics encompassed within it (summarization, full-text search, sentiment analysis, content categorization, etc.) is one of fastest growing, most complex and most highly demanded knowledge sets in the software industry today.
From spell checking in your SMS client to programmatically evaluating what your Twitter followers think of you, there is no shortage of real-world text processing and linguistic analysis problems all around us waiting to be solved. As Rubyists and software engineers, its important for us to know what tools related to NLP are available to us and how we can make use of them most effectively.

While there are a number of really great open-source libraries for natural language processing in Ruby and many great strides have been made in recent years, there's still often a need to leverage tools and libraries externally from the Ruby ecosystem. Some of the best open-source NLP frameworks available rely very heavily on contributions from the academic world where Ruby as a language doesn't have the same presence as other languages like Python or Java.
In this talk, I'll provide a beginner friendly introduction to NLP in general and I'll give a quick overview of the tools and related projects that are currently available in the Ruby community. In addition, using real-world examples I'll demonstrate how to painlessly leverage high performance, mature and well-established NLP libraries directly from your Ruby application using JRuby and JDK 7.

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The video titled "Natural Language Processing with Ruby" presented by Brandon Black at Rails Conf 2013 offers a beginner-friendly introduction to Natural Language Processing (NLP) and its relevance to software engineering, particularly within the Ruby community. It outlines challenges and tools associated with NLP, emphasizing the need for Ruby developers to engage with this evolving field. The presentation is structured around the following key points:

- **Definition of NLP**: NLP is the field concerned with making computers understand and generate human language, involving tasks like searching, parsing, and generating text.
- **Applications and Challenges**: It covers several applications of NLP such as search engines, spell checking, and sentiment analysis, and highlights the complexities involved, including linguistic nuances and evolving language.
- **Importance in the Software Industry**: The speaker explains that a significant portion of worker productivity is spent searching for information, making NLP solutions critical for improving efficiency in data handling.
- **NLP Solutions and Tools**: Brandon presents existing Ruby libraries conducive to NLP, including Chronic for date processing and Treat, which aims to mirror Python's NLP toolkit but points out the lack of comprehensive solutions compared to other programming languages like Python and Java. He stresses the importance of leveraging these tools effectively.
- **Role of JRuby**: For more advanced NLP tasks, he promotes using JRuby, as it allows Ruby applications to utilize mature Java NLP libraries, which are substantially developed and well-documented.
- **Encouragement to Engage**: Lastly, Brandon encourages Rubyists to contribute to existing libraries, learn from other programming communities, and partake in educational resources, highlighting courses from Stanford and practical literature on NLP.

In conclusion, the talk underscores the critical role of NLP in enhancing computational linguistics and motivates Ruby developers to deepen their involvement in this essential aspect of computer science, stressing that Ruby can play a significant role in this space despite current limitations.

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