Search Results for author: Negar Arabzadeh

Found 7 papers, 1 papers with code

Unsupervised Question Clarity Prediction Through Retrieved Item Coherency

no code implementations9 Aug 2022 Negar Arabzadeh, Mahsa Seifikar, Charles L. A. Clarke

While the research community has paid substantial attention to the problem of predicting query ambiguity in traditional search contexts, researchers have paid relatively little attention to predicting when this ambiguity is sufficient to warrant clarification in the context of conversational systems.

Conversational Question Answering

Early Stage Sparse Retrieval with Entity Linking

no code implementations9 Aug 2022 Dahlia Shehata, Negar Arabzadeh, Charles L. A. Clarke

In this work, we propose boosting the performance of sparse retrievers by expanding both the queries and the documents with linked entities in two formats for the entity names: 1) explicit and 2) hashed.

Entity Linking Information Retrieval

PREME: Preference-based Meeting Exploration through an Interactive Questionnaire

no code implementations5 May 2022 Negar Arabzadeh, Ali Ahmadvand, Julia Kiseleva, Yang Liu, Ahmed Hassan Awadallah, Ming Zhong, Milad Shokouhi

The recent increase in the volume of online meetings necessitates automated tools for managing and organizing the material, especially when an attendee has missed the discussion and needs assistance in quickly exploring it.

Supporting Complex Information-Seeking Tasks with Implicit Constraints

no code implementations2 May 2022 Ali Ahmadvand, Negar Arabzadeh, Julia Kiseleva, Patricio Figueroa Sanz, Xin Deng, Sujay Jauhar, Michael Gamon, Eugene Agichtein, Ned Friend, Aniruddha

Current interactive systems with natural language interface lack an ability to understand a complex information-seeking request which expresses several implicit constraints at once, and there is no prior information about user preferences, e. g., "find hiking trails around San Francisco which are accessible with toddlers and have beautiful scenery in summer", where output is a list of possible suggestions for users to start their exploration.

Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection

no code implementations22 Sep 2021 Negar Arabzadeh, Xinyi Yan, Charles L. A. Clarke

These hybrid retrievers leverage low-cost, exact-matching based sparse retrievers along with dense retrievers to bridge the semantic gaps between query and documents.

Information Retrieval

Shallow pooling for sparse labels

1 code implementation31 Aug 2021 Negar Arabzadeh, Alexandra Vtyurina, Xinyi Yan, Charles L. A. Clarke

To test this observation, we employed crowdsourced workers to make preference judgments between the top item returned by a modern neural ranking stack and a judged relevant item.

Passage Ranking

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