Boost
Light Mode Dark Mode
News Learn Community Libraries Releases
User Guide Contributor Guide Formal Reviews

Natural Language Processing

Developing a natural language parsing model involves tasks such as tokenization, parsing, and possibly dealing with ambiguous or erroneous inputs.

Natural language processing is a complex field that goes beyond just programming. It involves knowledge of linguistics, and many parsing tasks can be difficult due to the ambiguity and complexity of natural languages. You may want to look into existing tools and libraries specifically designed for natural language processing, like NLTK or SpaCy for Python, in addition to the general-purpose libraries that Boost offers.

Libraries

  • Boost.Spirit: This is Boost’s library for creating parsers and output generation. It includes support for creating grammars, which can be used to define the structure of sentences in English, or other language. You can use it to tokenize and parse input text according to your grammars.

  • Boost.Regex: For some simpler parsing tasks, regular expressions can be sufficient and easier to use than full-blown parsing libraries. You could use Boost.Regex to match specific patterns in your input text, like specific words or phrases, word boundaries, etc.

  • Boost.String_Algo: Provides various string manipulation algorithms, such as splitting strings, trimming whitespace, or replacing substrings. These can be useful in preprocessing text before parsing it.

  • Boost.Optional and Boost.Variant: These libraries can be helpful in representing the result of a parsing operation, which could be a successful parse (yielding a specific result), an error, or an ambiguous parse (yielding multiple possible results).

  • Boost.MultiIndex: Provides data structures that can be indexed in multiple ways. It can be useful for storing and retrieving information about words or phrases in your language model, such as part of speech, frequency, or possible meanings.

  • Boost.PropertyTree or Boost.Json: These libraries can be useful for handling input and output, such as reading configuration files or producing structured output.

Natural Language Processing Applications

Natural language parsing (NLP) is a foundational technology that powers many more applications and is a hot area of research and development.

  • Search Engines: Search engines like Google extensively use natural language processing to understand the intent behind user queries and provide accurate and relevant search results.

  • Machine Translation: Services like Google Translate and DeepL rely on natural language parsing for their operation. These services can now translate text between many different languages with surprising accuracy.

  • Speech Recognition: Virtual assistants like Apple’s Siri, Amazon’s Alexa, Google Assistant, and Microsoft’s Cortana, use natural language parsing to understand voice commands from users.

  • Text Summarization: This is used in a variety of contexts, from generating summaries of news articles, scientific papers, to creating summaries of lengthy documents for quicker reading.

  • Sentiment Analysis: Businesses use NLP to gauge public opinion about their products and services by analyzing social media posts, customer reviews, and other user-generated content.

  • Chatbots and Virtual Assistants: NLP is key to the operation of automated chatbots, which are used for everything from customer service to mental health support.

  • Information Extraction: Companies use NLP to extract specific information from unstructured text, like dates, names, or specific keywords.

  • Text-to-Speech (TTS): Services that convert written text into spoken words often use natural language processing to ensure proper pronunciation, intonation, and timing.

  • Grammar Checkers: Tools like Grammarly use natural language processing to correct grammar, punctuation, and style in written text.

  • Content Recommendation: Platforms like YouTube or Netflix use NLP to analyze the content and provide more accurate and personalized recommendations.