21,605 research outputs found
Efficient & Effective Selective Query Rewriting with Efficiency Predictions
To enhance effectiveness, a user's query can be rewritten internally by the search engine in many ways, for example by applying proximity, or by expanding the query with related terms. However, approaches that benefit effectiveness often have a negative impact on efficiency, which has impacts upon the user satisfaction, if the query is excessively slow. In this paper, we propose a novel framework for using the predicted execution time of various query rewritings to select between alternatives on a per-query basis, in a manner that ensures both effectiveness and efficiency. In particular, we propose the prediction of the execution time of ephemeral (e.g., proximity) posting lists generated from uni-gram inverted index posting lists, which are used in establishing the permissible query rewriting alternatives that may execute in the allowed time. Experiments examining both the effectiveness and efficiency of the proposed approach demonstrate that a 49% decrease in mean response time (and 62% decrease in 95th-percentile response time) can be attained without significantly hindering the effectiveness of the search engine
Learning Term Weights for Ad-hoc Retrieval
Most Information Retrieval models compute the relevance score of a document
for a given query by summing term weights specific to a document or a query.
Heuristic approaches, like TF-IDF, or probabilistic models, like BM25, are used
to specify how a term weight is computed. In this paper, we propose to leverage
learning-to-rank principles to learn how to compute a term weight for a given
document based on the term occurrence pattern
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
Training Curricula for Open Domain Answer Re-Ranking
In precision-oriented tasks like answer ranking, it is more important to rank
many relevant answers highly than to retrieve all relevant answers. It follows
that a good ranking strategy would be to learn how to identify the easiest
correct answers first (i.e., assign a high ranking score to answers that have
characteristics that usually indicate relevance, and a low ranking score to
those with characteristics that do not), before incorporating more complex
logic to handle difficult cases (e.g., semantic matching or reasoning). In this
work, we apply this idea to the training of neural answer rankers using
curriculum learning. We propose several heuristics to estimate the difficulty
of a given training sample. We show that the proposed heuristics can be used to
build a training curriculum that down-weights difficult samples early in the
training process. As the training process progresses, our approach gradually
shifts to weighting all samples equally, regardless of difficulty. We present a
comprehensive evaluation of our proposed idea on three answer ranking datasets.
Results show that our approach leads to superior performance of two leading
neural ranking architectures, namely BERT and ConvKNRM, using both pointwise
and pairwise losses. When applied to a BERT-based ranker, our method yields up
to a 4% improvement in MRR and a 9% improvement in P@1 (compared to the model
trained without a curriculum). This results in models that can achieve
comparable performance to more expensive state-of-the-art techniques.Comment: Accepted at SIGIR 2020 (long
Recommender Systems by means of Information Retrieval
In this paper we present a method for reformulating the Recommender Systems
problem in an Information Retrieval one. In our tests we have a dataset of
users who give ratings for some movies; we hide some values from the dataset,
and we try to predict them again using its remaining portion (the so-called
"leave-n-out approach"). In order to use an Information Retrieval algorithm, we
reformulate this Recommender Systems problem in this way: a user corresponds to
a document, a movie corresponds to a term, the active user (whose rating we
want to predict) plays the role of the query, and the ratings are used as
weigths, in place of the weighting schema of the original IR algorithm. The
output is the ranking list of the documents ("users") relevant for the query
("active user"). We use the ratings of these users, weighted according to the
rank, to predict the rating of the active user. We carry out the comparison by
means of a typical metric, namely the accuracy of the predictions returned by
the algorithm, and we compare this to the real ratings from users. In our first
tests, we use two different Information Retrieval algorithms: LSPR, a recently
proposed model based on Discrete Fourier Transform, and a simple vector space
model
A study of query expansion methods for patent retrieval
Patent retrieval is a recall-oriented search task where the objective is to find all possible relevant documents. Queries in patent retrieval are typically very long since they take the form of a patent claim or even a full patent application in the case of priorart patent search. Nevertheless, there is generally a significant mismatch between the query and the relevant documents, often leading to low retrieval effectiveness. Some previous work has
tried to address this mismatch through the application of query expansion (QE) techniques which have generally showed
effectiveness for many other retrieval tasks. However, results of QE on patent search have been found to be very disappointing. We present a review of previous investigations of QE in patent retrieval, and explore some of these techniques on a prior-art patent search task. In addition, a novel method for QE using automatically generated synonyms set is presented. While previous QE techniques fail to improve over baseline retrieval, our new approach show statistically better retrieval precision over
the baseline, although not for recall. In addition, it proves to be significantly more efficient than existing techniques. An extensive analysis to the results is presented which seeks to better understand situations where these QE techniques succeed or fail
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