17,475 research outputs found
Ad Hoc Table Retrieval using Semantic Similarity
We introduce and address the problem of ad hoc table retrieval: answering a
keyword query with a ranked list of tables. This task is not only interesting
on its own account, but is also being used as a core component in many other
table-based information access scenarios, such as table completion or table
mining. The main novel contribution of this work is a method for performing
semantic matching between queries and tables. Specifically, we (i) represent
queries and tables in multiple semantic spaces (both discrete sparse and
continuous dense vector representations) and (ii) introduce various similarity
measures for matching those semantic representations. We consider all possible
combinations of semantic representations and similarity measures and use these
as features in a supervised learning model. Using a purpose-built test
collection based on Wikipedia tables, we demonstrate significant and
substantial improvements over a state-of-the-art baseline.Comment: The web conference 2018 (WWW'18
A Deep Relevance Matching Model for Ad-hoc Retrieval
In recent years, deep neural networks have led to exciting breakthroughs in
speech recognition, computer vision, and natural language processing (NLP)
tasks. However, there have been few positive results of deep models on ad-hoc
retrieval tasks. This is partially due to the fact that many important
characteristics of the ad-hoc retrieval task have not been well addressed in
deep models yet. Typically, the ad-hoc retrieval task is formalized as a
matching problem between two pieces of text in existing work using deep models,
and treated equivalent to many NLP tasks such as paraphrase identification,
question answering and automatic conversation. However, we argue that the
ad-hoc retrieval task is mainly about relevance matching while most NLP
matching tasks concern semantic matching, and there are some fundamental
differences between these two matching tasks. Successful relevance matching
requires proper handling of the exact matching signals, query term importance,
and diverse matching requirements. In this paper, we propose a novel deep
relevance matching model (DRMM) for ad-hoc retrieval. Specifically, our model
employs a joint deep architecture at the query term level for relevance
matching. By using matching histogram mapping, a feed forward matching network,
and a term gating network, we can effectively deal with the three relevance
matching factors mentioned above. Experimental results on two representative
benchmark collections show that our model can significantly outperform some
well-known retrieval models as well as state-of-the-art deep matching models.Comment: CIKM 2016, long pape
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
Table Search Using a Deep Contextualized Language Model
Pretrained contextualized language models such as BERT have achieved
impressive results on various natural language processing benchmarks.
Benefiting from multiple pretraining tasks and large scale training corpora,
pretrained models can capture complex syntactic word relations. In this paper,
we use the deep contextualized language model BERT for the task of ad hoc table
retrieval. We investigate how to encode table content considering the table
structure and input length limit of BERT. We also propose an approach that
incorporates features from prior literature on table retrieval and jointly
trains them with BERT. In experiments on public datasets, we show that our best
approach can outperform the previous state-of-the-art method and BERT baselines
with a large margin under different evaluation metrics.Comment: Accepted at SIGIR 2020 (Long
Pairwise similarity of TopSig document signatures
This paper analyses the pairwise distances of signatures produced by the TopSig retrieval model on two document collections. The distribution of the distances are compared to purely random signatures. It explains why TopSig is only competitive with state of the art retrieval models at early precision. Only the local neighbourhood of the signatures is interpretable. We suggest this is a common property of vector space models
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
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