1,479 research outputs found
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
Relevance-based Word Embedding
Learning a high-dimensional dense representation for vocabulary terms, also
known as a word embedding, has recently attracted much attention in natural
language processing and information retrieval tasks. The embedding vectors are
typically learned based on term proximity in a large corpus. This means that
the objective in well-known word embedding algorithms, e.g., word2vec, is to
accurately predict adjacent word(s) for a given word or context. However, this
objective is not necessarily equivalent to the goal of many information
retrieval (IR) tasks. The primary objective in various IR tasks is to capture
relevance instead of term proximity, syntactic, or even semantic similarity.
This is the motivation for developing unsupervised relevance-based word
embedding models that learn word representations based on query-document
relevance information. In this paper, we propose two learning models with
different objective functions; one learns a relevance distribution over the
vocabulary set for each query, and the other classifies each term as belonging
to the relevant or non-relevant class for each query. To train our models, we
used over six million unique queries and the top ranked documents retrieved in
response to each query, which are assumed to be relevant to the query. We
extrinsically evaluate our learned word representation models using two IR
tasks: query expansion and query classification. Both query expansion
experiments on four TREC collections and query classification experiments on
the KDD Cup 2005 dataset suggest that the relevance-based word embedding models
significantly outperform state-of-the-art proximity-based embedding models,
such as word2vec and GloVe.Comment: to appear in the proceedings of The 40th International ACM SIGIR
Conference on Research and Development in Information Retrieval (SIGIR '17
Neural Ranking Models with Weak Supervision
Despite the impressive improvements achieved by unsupervised deep neural
networks in computer vision and NLP tasks, such improvements have not yet been
observed in ranking for information retrieval. The reason may be the complexity
of the ranking problem, as it is not obvious how to learn from queries and
documents when no supervised signal is available. Hence, in this paper, we
propose to train a neural ranking model using weak supervision, where labels
are obtained automatically without human annotators or any external resources
(e.g., click data). To this aim, we use the output of an unsupervised ranking
model, such as BM25, as a weak supervision signal. We further train a set of
simple yet effective ranking models based on feed-forward neural networks. We
study their effectiveness under various learning scenarios (point-wise and
pair-wise models) and using different input representations (i.e., from
encoding query-document pairs into dense/sparse vectors to using word embedding
representation). We train our networks using tens of millions of training
instances and evaluate it on two standard collections: a homogeneous news
collection(Robust) and a heterogeneous large-scale web collection (ClueWeb).
Our experiments indicate that employing proper objective functions and letting
the networks to learn the input representation based on weakly supervised data
leads to impressive performance, with over 13% and 35% MAP improvements over
the BM25 model on the Robust and the ClueWeb collections. Our findings also
suggest that supervised neural ranking models can greatly benefit from
pre-training on large amounts of weakly labeled data that can be easily
obtained from unsupervised IR models.Comment: In proceedings of The 40th International ACM SIGIR Conference on
Research and Development in Information Retrieval (SIGIR2017
Learning to merge search results for efficient Distributed Information Retrieval
Merging search results from different servers is a major problem in Distributed Information Retrieval. We used Regression-SVM and Ranking-SVM which would learn a function that merges results based on information that is readily available: i.e. the ranks, titles, summaries and URLs contained in the results pages. By not downloading additional information, such as the full document, we decrease bandwidth usage. CORI and Round Robin merging were used as our baselines; surprisingly, our results show that the SVM-methods do not improve over those baselines
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
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
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
Probabilistic learning for selective dissemination of information
New methods and new systems are needed to filter or to selectively distribute the increasing volume of electronic information being produced nowadays. An effective information filtering system is one that provides the exact information that fulfills user's interests with the minimum effort by the user to describe it. Such a system will have to be adaptive to the user changing interest. In this paper we describe and evaluate a learning model for information filtering which is an adaptation of the generalized probabilistic model of information retrieval. The model is based on the concept of 'uncertainty sampling', a technique that allows for relevance feedback both on relevant and nonrelevant documents. The proposed learning model is the core of a prototype information filtering system called ProFile
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