NLP in Search Engine
Before using natural language processing libraries, traditional search methods could also compare search entries and results. For example, in one of my previous projects, I conducted preliminary fuzzy searches using SQL’s like statement and wrote a string similarity calculation method based on dynamic programming. By calculating the similarity between the input text and the preliminary search results, a ranking mechanism was established. If the search object has multiple attributes, it is also necessary to configure the weight distribution of the attributes.
After completing the traditional search engine, we need to conduct a lot of testing, adjust the similarity algorithm, and attribute weighting. This is to make the search engine conform to the usage habits of most people.
Traditional search engines have many disadvantages. For example, the matching degree of words is determined by alphabetical order, which sometimes cannot accurately express the meaning of words. For instance, when users search for “Apple Laptop,” they obviously want to find products related to MacBook. However, it’s unclear whether users are searching by brand name or product name (“Apple” is the brand, “MacBook” is the product). When comparing “MacBook” with “Apple Laptop,” we find a low similarity, which is not what we want. Therefore, we still need a lot of testing to distribute weights for brand names, product names, models, and other attributes.
However, work becomes simpler after introducing NLP libraries. Before their release, NLP libraries used machine learning to perform a lot of semantic computation and similarity mapping. This helps us change the original approach; we no longer need to use dynamic programming to deal with word similarity, nor do we need to weight attributes, unless some attributes are really rare. We just need to call the NLP equation to calculate semantic similarity, and then use a similar ranking to get results.
Next comes the testing phase. In conventional testing, we need to employ a large number of people to manually score search results to improve the accuracy of the search engine. However, with a development framework equipped with natural language processing, these tedious testing steps are unnecessary. We can use the results of machine learning to measure search accuracy in bulk, significantly reducing labor costs.
For Teamcal, the use of the NLP framework is also an upcoming prospect. For example, in your complex project and large team collaboration, it is necessary to accurately select the right project members for meetings and arrangements. Each team member’s complex sectors, work content, and experience will be stored in the database. We can use the NLP framework to semantically compare the project theme with team members’ data, sort, and recommend members you may want to choose, helping you reduce the time needed to accurately select project-related members.
As you use this function more, the product’s machine learning framework will come into play. NLP continuously learns from user behavior, improving the accuracy of the framework’s push.
As a powerful tool for improving schedule planning and personnel dispatch efficiency, Teamcal.ai has a strong AI development and engineering team. We believe that in the near future, products equipped with the NLP framework will improve the efficiency of all users’ schedule arrangements.
