181 research outputs found
Aggregated search: a new information retrieval paradigm
International audienceTraditional search engines return ranked lists of search results. It is up to the user to scroll this list, scan within different documents and assemble information that fulfill his/her information need. Aggregated search represents a new class of approaches where the information is not only retrieved but also assembled. This is the current evolution in Web search, where diverse content (images, videos, ...) and relational content (similar entities, features) are included in search results. In this survey, we propose a simple analysis framework for aggregated search and an overview of existing work. We start with related work in related domains such as federated search, natural language generation and question answering. Then we focus on more recent trends namely cross vertical aggregated search and relational aggregated search which are already present in current Web search
Using Implicit Feedback to Improve Question Generation
Question Generation (QG) is a task of Natural Language Processing (NLP) that
aims at automatically generating questions from text. Many applications can
benefit from automatically generated questions, but often it is necessary to
curate those questions, either by selecting or editing them. This task is
informative on its own, but it is typically done post-generation, and, thus,
the effort is wasted. In addition, most existing systems cannot incorporate
this feedback back into them easily. In this work, we present a system, GEN,
that learns from such (implicit) feedback. Following a pattern-based approach,
it takes as input a small set of sentence/question pairs and creates patterns
which are then applied to new unseen sentences. Each generated question, after
being corrected by the user, is used as a new seed in the next iteration, so
more patterns are created each time. We also take advantage of the corrections
made by the user to score the patterns and therefore rank the generated
questions. Results show that GEN is able to improve by learning from both
levels of implicit feedback when compared to the version with no learning,
considering the top 5, 10, and 20 questions. Improvements go up from 10%,
depending on the metric and strategy used.Comment: 27 pages, 8 figure
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