Search Results for author:
Found 17 papers, 7 papers with code
Unsupervised, Efficient and Semantic Expertise Retrieval
We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches.
Structural Regularities in Text-based Entity Vector Spaces
We discover how clusterings of experts correspond to committees in organizations, the ability of expert representations to encode the co-author graph, and the degree to which they encode academic rank.
Semantic Entity Retrieval Toolkit
Unsupervised learning of low-dimensional, semantic representations of words and entities has recently gained attention.
Learning Latent Vector Spaces for Product Search
We introduce a novel latent vector space model that jointly learns the latent representations of words, e-commerce products and a mapping between the two without the need for explicit annotations.
Pyndri: a Python Interface to the Indri Search Engine
We introduce pyndri, a Python interface to the Indri search engine.
Neural Vector Spaces for Unsupervised Information Retrieval
We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval.
Lexical Query Modeling in Session Search
Lexical query modeling has been the leading paradigm for session search.
Reply With: Proactive Recommendation of Email Attachments
Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message.
Remedies against the Vocabulary Gap in Information Retrieval
Search engines rely heavily on term-based approaches that represent queries and documents as bags of words.
Neural Networks for Information Retrieval
Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them.
Connecting and Comparing Language Model Interpolation Techniques
In this work, we uncover a theoretical connection between two language model interpolation techniques, count merging and Bayesian interpolation.
Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants
We focus on improving the effectiveness of a Virtual Assistant (VA) in recognizing emerging entities in spoken queries.
Error-driven Pruning of Language Models for Virtual Assistants
We customize entropy pruning by allowing for a keep list of infrequent n-grams that require a more relaxed pruning threshold, and propose three methods to construct the keep list.
A Discriminative Entity-Aware Language Model for Virtual Assistants
High-quality automatic speech recognition (ASR) is essential for virtual assistants (VAs) to work well.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Space-Efficient Representation of Entity-centric Query Language Models
Virtual assistants make use of automatic speech recognition (ASR) to help users answer entity-centric queries.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Modeling Spoken Information Queries for Virtual Assistants: Open Problems, Challenges and Opportunities
Virtual assistants are becoming increasingly important speech-driven Information Retrieval platforms that assist users with various tasks.
Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants
On-device Virtual Assistants (VAs) powered by Automatic Speech Recognition (ASR) require effective knowledge integration for the challenging entity-rich query recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
