Natural Language Processing

Natural Language Processing

Natural language processing is anarena of Artificial Intelligence, computer science and computational semantics concerned with the interactions between computers and human languages. Mainly, it is connected to area of human-computer interaction. Natural Language Processing is a method for processors to examine, recognize and grow meaning from human language in a smart and valuable way.

By utilizing Natural Language Processing (NLP) developers can establish and structure information to do tasks such as automatic summarization, conversion, named entity recognition, relationship mining, speech recognition, sentiment analysis and topic segmentation.

Natural Language Processing (NLP) is used to examine text and allow machines to appreciate how humans speak. This human-computer communication allows real-world applications like automatic text summarization, sentiment analysis, topic extraction, parts-of-speech tagging, named entity recognition, relationship extraction, stemming, and more. It is commonly used for machine translation, text mining and automated question answering.

Webtunix is a Machine Learning Company in India which uses state-of-the-art Natural Language Processing and Artificial Intelligence techniques to parse, analyze and extract semantic meta-data from your content. Our high performance machine learning stack was designed from the ground up for accuracy, speed and robustness across varied styles of writing. In addition we offer innovative web & enterprise solutions using Semantic Analysis and Text Analytics.

Natural Language Processing (NLP) is used for conversational interfaces that handle interactions between machines and humans in the preferred language of the human. It use machine learning to analyse patterns in data and continuously improve the program's own understanding.

Main features of NLP are:

1. Semantic search: It uses a number of signals to recognize the user’s intent and grip ambiguity. As an example, semantic search appreciates that when a user examines for “profit,” she would also want to find data sets that reference “net income.” Similarly, if a user searches for “CHD,” they would also return entries for “Chandigarh.”

2. Autocomplete: Itshows relevant and common search ideas as users type, saving time and obstruction. Webtunix bases offers on user activity and data exploration, creation the autocomplete suggestions highly targeted.

3. Correction of Spelling: It also raises the efficiency and speed of search. When aparticular match isn’t obtainable, it identifies the closest logical alternative.

4. Lemmatization: Lemmatization practices deviations on words such as tenses, plurals, genders, hyphenated forms, and many more.

5. Faceted search group: Faceted search group’s articles reverted from a request into the greatest appropriate sub sections.Users can improve their examine by puncturing down into a particular group or facet.

6. Advanced techniques: Advanced syntax growths precision from side to side techniques such as phrase search, Boolean matching, fielded search and proximity search.

7. Fuzzy matching: Fuzzy matching escalates recall and permits for moveable matching.Substring and fairly accuratecompetitions allow operators to catch data sources when they only have incompletedata or even incorrect information.

In Addition to English, Webtunix helps over 40+ languages along with state-of-the-art abilities containing statistical entity extraction and algorithmic entity individuality and extraction strategies for a huge variety of European, Middle Eastern and Asian languages.


  • Recognition and linguistic capabilities vital for high-quality seek
  • Stop phrase removal
  • Tokenization
  • Lemmatization/stemming
  • Decompounding