NLP pipeline: a necessary mediator between bots and customers

Many consulting companies are currently delivering chatbots that understand and reply to humans in a more natural way. Nevertheless, there is still scope for improvement and challenges to overcome. Chatbots must know how to deal with complex queries not to scare customers away. Therefore, those able to achieve this goal will turn into cash cows for their consulting firms.

Unless consulting companies are willing to lose their clients, they must consider making improvements in the understanding skills of these conversational agents either in-house or through external suppliers. In this context, it is important to know that achieving a more spontaneous interaction requires more time, money and knowledge. That’s why, the most effective way to avoid these hurdles is resorting directly to experts in Natural Language Processing techniques.

It is crucial to make customers feel that they are being heard and correctly understood to increase customer loyalty. That’s exactly what every competent company yearns for: someone (or something) to take care of their clients 24/7. A virtual assistant able to hold a natural conversation is worth its weight in gold due to its capacity to get along with customers as a flesh-and-blood agent would do. This requirement is, therefore, mandatory for an outstanding sales growth. In fact, a South American insurance company called elMejorTrato serves as a good example, since it embedded a chatbot platform that fully automated 76.9 percent of all customer queries resulting in a 21.5 percent increase in sales.

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NLP Pipeline to make your bot understand better

This is all well and good on paper, but how exactly can a NLP pipeline resolve the big challenge of turning a bot into a human-like speaking machine? Let Bitext technology clear the following obstacles out of the way for you:

  • Misspelling. You will need a spell checker so that your bot can recognize inputs such as Can I get a hut dog? or Can I get dog jdhgf hot? Bitext NLP tool will catch all raw data from the user and fix any typo on the way.
  • Chunks of text. Users tend to tell the bot their life story. This fact makes it very difficult for bots to understand what they actually need. You will need a segmentation tool that splits the text into sentences for a further individual analysis to make him digest it better.
  • One word, multiple meanings. ‘Reading a book when in a hotel room’ is not the same as ‘To book a hotel room when reading’. Some words are spelled alike but have different meanings. Here comes the POS (part-of-speech) Tagging tool helping to disambiguate several meanings.
  • One root, multiple inflections. A simple word may present different forms, as for instance, ‘order – ordering – ordered’. Bitext Lemmatization tool helps here detect every root to simplify the rules.
  • Dates, numbers, proper nouns. Dates and numbers can be written in different formats: 3/1/2011, 1st of March, next Wednesday, 2016–03–01, 123, one hundred, etc. An identification of such entities is quite helpful for a bot to classify them and unify their format. Bitext Entity Extraction tool automatically finds names of people, places or products, among others, in texts facilitating that process.
As you can see, NLP solutions are fundamental for a bot to understand users correctly and offer them the most suitable service according to their needs. Anyway, these solutions demand too many technical resources that not every company has at disposal. As Gartner says, choosing an NLP vendor has come up to be a high priority. Bitext enables enterprises to be technology-agnostic when choosing an NLP vendor. This recommended option will reduce their workload if they want to switch platforms or customize on a broad scale in the future. Want to try it? One look is worth a thousand words – take it now by clicking down here.

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