Regularized Multimodal Hierarchical Topic Model for Document-by-Document Exploratory Search

In the exploratory search paradigm of information retrieval, the user has a complicated search demand that can not be formulated in a short query. The user collects thematically relevant information iteratively in a “query-browse-refine” process being motivated by learning, understanding, and knowledge acquisition purposes. We consider an elementary step of this scenario in which the search intent can be expressed by a long text query. For this case, we develop an exploratory search engine based on probabilistic topic modeling. Topic model gives a low-dimensional sparse interpretable vector representation (topical embedding) of a text. The search engine uses these embeddings for ranking documents by their similarity to the query. We show that performing only one query, the topic-based search engine achieves better precision and recall that human assessors do spending up to one hour in a conventional browse-refine loop. We use additive regularization for topic modeling (ARTM) to make the model simultaneously sparse, decorrelated, n-gram, multimodal and hierarchical. We show experimentally that each of these features of the model is important to achieve precision and recall higher than 90%. Topical hierarchy emulates a natural human strategy to focus on subtopics gradually discarding unnecessary information. Also we show that increasing the number of levels in the hierarchy improves the search quality and makes it possible to enrich the model with a larger number of topics. We use the fast parallel implementation of the regularized EM-algorithm from BigARTM open source project. We use crowdsourcing in order to collect relevance assessments for the search quality evaluation.