March 2021 Summaries

The Haystack project aims to build a semantic search engine that can adapt to user information needs and way of speaking, moving beyond keyword-based systems. By incorporating state-of-the-art NLP methods, such as retriever-reader pipelines, answer generators, summarizers, and flexible pipelines, Haystack enables developers to create bespoke natural language interfaces into text data. The project's goal is to provide a more efficient and accurate search experience, allowing users to ask complex full-sentence questions and receive curated responses. With ongoing research and development, Haystack plans to incorporate multilingual and domain-specific language technologies, further expanding its capabilities.

Semantic search is a technology that allows search engines to better understand user intent and provide more relevant results. It uses natural language processing (NLP) techniques and knowledge graphs to analyze the context of a query, rather than just relying on keyword-based matching. This enables search engines to handle unstructured textual data and match documents to queries based on semantics rather than lexical overlap. Semantic search can overcome the limits of traditional keyword-based approaches and improve informational search results. It is particularly useful for navigational search queries where the intent is to find a specific point or destination, but may not be sufficient for informational search queries where the intent is to access knowledge directly. The creation of semantic search engines like Haystack enables users to interact with enterprise knowledge in a more natural and flexible way, using conversational language and interfaces that are closer to virtual assistants.