1,796 research outputs found
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
This paper proposes K-NRM, a kernel based neural model for document ranking.
Given a query and a set of documents, K-NRM uses a translation matrix that
models word-level similarities via word embeddings, a new kernel-pooling
technique that uses kernels to extract multi-level soft match features, and a
learning-to-rank layer that combines those features into the final ranking
score. The whole model is trained end-to-end. The ranking layer learns desired
feature patterns from the pairwise ranking loss. The kernels transfer the
feature patterns into soft-match targets at each similarity level and enforce
them on the translation matrix. The word embeddings are tuned accordingly so
that they can produce the desired soft matches. Experiments on a commercial
search engine's query log demonstrate the improvements of K-NRM over prior
feature-based and neural-based states-of-the-art, and explain the source of
K-NRM's advantage: Its kernel-guided embedding encodes a similarity metric
tailored for matching query words to document words, and provides effective
multi-level soft matches
Query Expansion with Locally-Trained Word Embeddings
Continuous space word embeddings have received a great deal of attention in
the natural language processing and machine learning communities for their
ability to model term similarity and other relationships. We study the use of
term relatedness in the context of query expansion for ad hoc information
retrieval. We demonstrate that word embeddings such as word2vec and GloVe, when
trained globally, underperform corpus and query specific embeddings for
retrieval tasks. These results suggest that other tasks benefiting from global
embeddings may also benefit from local embeddings
Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge
Citation texts are sometimes not very informative or in some cases inaccurate
by themselves; they need the appropriate context from the referenced paper to
reflect its exact contributions. To address this problem, we propose an
unsupervised model that uses distributed representation of words as well as
domain knowledge to extract the appropriate context from the reference paper.
Evaluation results show the effectiveness of our model by significantly
outperforming the state-of-the-art. We furthermore demonstrate how an effective
contextualization method results in improving citation-based summarization of
the scientific articles.Comment: SIGIR 201
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. The fast pace of modern-day research has
given rise to many different approaches for many different IR problems. The
amount of information available can be overwhelming both for junior students
and for experienced researchers looking for new research topics and directions.
Additionally, it is interesting to see what key insights into IR problems the
new technologies are able to give us. The aim of this full-day tutorial is to
give a clear overview of current tried-and-trusted neural methods in IR and how
they benefit IR research. It covers key architectures, as well as the most
promising future directions.Comment: Overview of full-day tutorial at SIGIR 201
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