29,388 research outputs found
MIR task and evaluation techniques
Existing tasks in MIREX have traditionally focused on low-level MIR tasks working with flat (usually DSP-only) ground-truth. These evaluation techniques, however, can not evaluate the increasing number of algorithms that utilize relational data and are not currently utilizing the state of the art in evaluating ranked or ordered output. This paper summarizes the state of the art in evaluating relational ground-truth. These components are then synthesized into novel evaluation techniques that are then applied to 14 concrete music document retrieval tasks, demonstrating how these evaluation techniques can be applied in a practical context
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
Content-Based Weak Supervision for Ad-Hoc Re-Ranking
One challenge with neural ranking is the need for a large amount of
manually-labeled relevance judgments for training. In contrast with prior work,
we examine the use of weak supervision sources for training that yield pseudo
query-document pairs that already exhibit relevance (e.g., newswire
headline-content pairs and encyclopedic heading-paragraph pairs). We also
propose filtering techniques to eliminate training samples that are too far out
of domain using two techniques: a heuristic-based approach and novel supervised
filter that re-purposes a neural ranker. Using several leading neural ranking
architectures and multiple weak supervision datasets, we show that these
sources of training pairs are effective on their own (outperforming prior weak
supervision techniques), and that filtering can further improve performance.Comment: SIGIR 2019 (short paper
WISER: A Semantic Approach for Expert Finding in Academia based on Entity Linking
We present WISER, a new semantic search engine for expert finding in
academia. Our system is unsupervised and it jointly combines classical language
modeling techniques, based on text evidences, with the Wikipedia Knowledge
Graph, via entity linking.
WISER indexes each academic author through a novel profiling technique which
models her expertise with a small, labeled and weighted graph drawn from
Wikipedia. Nodes in this graph are the Wikipedia entities mentioned in the
author's publications, whereas the weighted edges express the semantic
relatedness among these entities computed via textual and graph-based
relatedness functions. Every node is also labeled with a relevance score which
models the pertinence of the corresponding entity to author's expertise, and is
computed by means of a proper random-walk calculation over that graph; and with
a latent vector representation which is learned via entity and other kinds of
structural embeddings derived from Wikipedia.
At query time, experts are retrieved by combining classic document-centric
approaches, which exploit the occurrences of query terms in the author's
documents, with a novel set of profile-centric scoring strategies, which
compute the semantic relatedness between the author's expertise and the query
topic via the above graph-based profiles.
The effectiveness of our system is established over a large-scale
experimental test on a standard dataset for this task. We show that WISER
achieves better performance than all the other competitors, thus proving the
effectiveness of modelling author's profile via our "semantic" graph of
entities. Finally, we comment on the use of WISER for indexing and profiling
the whole research community within the University of Pisa, and its application
to technology transfer in our University
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