Google became who they are today by doing one thing to reach the world’s epitome of excellence, the search engine.
Search is undoubtedly an integral part of the digital era. Your specific niche set of data for work or your profession is incredibly valuable today; yet most professionals underestimate the value of data, if it is accessible or retrievable at all. Very often, we are deceived by the word "research" to justify the inherent inefficiencies when accessing or retrieving information before analysis. Our human minds are built for processing complex information deeply but are not designed to “read” through large amount of noise due to natural fatigue.
When was the last time a particular detail, term or name triggered an echo of a previous case or template, only to be unable to materialize as more than a word, or a name? Human memory is fallible, after all - but that is where technology can help. Artificial intelligence (AI) and Natural Language Processing (NLP) are buzzwords that get thrown around a lot, but what can “intelligent” search functions really do for you?
The value of data lies in how you use it. It doesn’t matter if you have access to all the data in the world if it is just sitting around, disorganized and unusable. Effective data search plays an integral role in internal knowledge management as well as data collection from external data sources. Not only does it ensure that you can find what you need when you need it, imagine the time that can be saved by going through vast quantities of information that is available for your professional services or strategic planning.
So where do concepts such as AI and NLP fit in? NLP is a sub-field of AI that allows you to discover content based on relevant context and intent, even if it is not part of the explicit wording of the search. This improves the depth and accuracy of search results, reducing the time and labour required for research. The best search engines combine both search by “Boolean operators” (such as AND, OR, AND NOT) together with NLP search that understands natural language.
Furthermore, the relevance of the set of data varies from industry to industry. Someone working in the legal profession will conduct searches with very different contexts and intents in mind than someone working in a marketing firm. However “smart” a search engine is, its effectiveness depends on the design and thorough understanding of such contexts.
An example is Named Entity Recognition (NER), also known as entity extraction or identification, a form of NLP that detects and categorises named entities. Entity categories could include names, organizations, locations, and field-specific categories such as legal terms and phrases. The more relevant the data is to the task, the more accurate the technology will be at completing it. NER developed from legal data will return more relevant results for ease of analysis.
The Cloudwork team has over five decades of experience in legal and financial services, giving us first hand knowledge of practitioners’ needs which we brought to the development of our NLP-based search engine with professional service practitioners in mind.
LELGALX’s search engine uses NLP to effectively retrieve documents, messages, tasks and file content with links to your projects or case matters. LEGALX’s search engine also covers as per request public knowledge base, giving you clarity to data under your research scope with clarity in seconds.