Learning Concept Embeddings for Query Expansion by Quantum Entropy Minimization
By Alessandro Sordoni, Yoshua Bengio, Jian-Yun Nie. 2014
50 citations recorded by the source corpus.
In web search, users queries are formulated using only few terms and term-matching retrieval functions could fail at retrieving relevant documents. Given a user query, the technique of query expansion (QE) consists in selecting related terms that could enhance the likelihood of retrieving relevant documents. Selecting such expansion terms is challenging and requires a computational framework capable of encoding complex semantic relationships. In this paper, we propose a novel method for learning, in a supervised way, semantic representations for words and phrases. By embedding queries and documents in special matrices, our model disposes of an increased representational power with respect to existing approaches adopting a vector representation. We show that our model produces high-quality query expansion terms. Our expansion increase IR measures beyond expansion from current word-embeddings models and well-established traditional QE methods.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Hamed Zamani
- W. Bruce Croft
- Rodrigo Nogueira
- Jamie Callan
- Jimmy J. Lin
- Kyunghyun Cho
- Byron C. Wallace
- Zhuyun Dai
In Information Systems
- Hamed Zamani
- Rodrigo Nogueira
- W. Bruce Croft
- Kyunghyun Cho
- Oren Kurland
- Jamie Callan
- Anna Shtok
- Saar Kuzi
Citations in the corpus
- Relevance-based Word Embedding (2017, methodology): “ery expansion using word embedding has been also studied in [11, 17, 35].”
- Task-Oriented Query Reformulation with Reinforcement Learning (2017, methodology): “An alternative technique based on supervised learning is to learn a common latent representation of queries and relevant documents terms by us-ing a click-through dataset (Sordoni et al., 2014).”
- Query Expansion Using Word Embeddings (2016, methodology): “Supervised concept-based embedding of n - grams was used for document retrieval [15].”
- Neural information retrieval: at the end of the early years (2017, methodology): “1 Query suggestion Learn Learn to generate Sordoni et al. (2015)”
- Embedding-based Query Language Models (2016, methodology): “[25] also proposed a query expansion method based on concept embedding.”
- Estimating Embedding Vectors for Queries (2016, methodology): “[33] employed word embedding techniques for expanding queries in a supervised manner.”