17,041 research outputs found
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. In the NVSM paradigm, we learn low-dimensional representations of
words and documents from scratch using gradient descent and rank documents
according to their similarity with query representations that are composed from
word representations. We show that NVSM performs better at document ranking
than existing latent semantic vector space methods. The addition of NVSM to a
mixture of lexical language models and a state-of-the-art baseline vector space
model yields a statistically significant increase in retrieval effectiveness.
Consequently, NVSM adds a complementary relevance signal. Next to semantic
matching, we find that NVSM performs well in cases where lexical matching is
needed.
NVSM learns a notion of term specificity directly from the document
collection without feature engineering. We also show that NVSM learns
regularities related to Luhn significance. Finally, we give advice on how to
deploy NVSM in situations where model selection (e.g., cross-validation) is
infeasible. We find that an unsupervised ensemble of multiple models trained
with different hyperparameter values performs better than a single
cross-validated model. Therefore, NVSM can safely be used for ranking documents
without supervised relevance judgments.Comment: TOIS 201
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search
Despite substantial interest in applications of neural networks to
information retrieval, neural ranking models have only been applied to standard
ad hoc retrieval tasks over web pages and newswire documents. This paper
proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network)
a novel neural ranking model specifically designed for ranking short social
media posts. We identify document length, informal language, and heterogeneous
relevance signals as features that distinguish documents in our domain, and
present a model specifically designed with these characteristics in mind. Our
model uses hierarchical convolutional layers to learn latent semantic
soft-match relevance signals at the character, word, and phrase levels. A
pooling-based similarity measurement layer integrates evidence from multiple
types of matches between the query, the social media post, as well as URLs
contained in the post. Extensive experiments using Twitter data from the TREC
Microblog Tracks 2011--2014 show that our model significantly outperforms prior
feature-based as well and existing neural ranking models. To our best
knowledge, this paper presents the first substantial work tackling search over
social media posts using neural ranking models.Comment: AAAI 2019, 10 page
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