426 research outputs found
From Review to Rating: Exploring Dependency Measures for Text Classification
Various text analysis techniques exist, which attempt to uncover unstructured
information from text. In this work, we explore using statistical dependence
measures for textual classification, representing text as word vectors. Student
satisfaction scores on a 3-point scale and their free text comments written
about university subjects are used as the dataset. We have compared two textual
representations: a frequency word representation and term frequency
relationship to word vectors, and found that word vectors provide a greater
accuracy. However, these word vectors have a large number of features which
aggravates the burden of computational complexity. Thus, we explored using a
non-linear dependency measure for feature selection by maximizing the
dependence between the text reviews and corresponding scores. Our quantitative
and qualitative analysis on a student satisfaction dataset shows that our
approach achieves comparable accuracy to the full feature vector, while being
an order of magnitude faster in testing. These text analysis and feature
reduction techniques can be used for other textual data applications such as
sentiment analysis.Comment: 8 page
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
This paper proposes K-NRM, a kernel based neural model for document ranking.
Given a query and a set of documents, K-NRM uses a translation matrix that
models word-level similarities via word embeddings, a new kernel-pooling
technique that uses kernels to extract multi-level soft match features, and a
learning-to-rank layer that combines those features into the final ranking
score. The whole model is trained end-to-end. The ranking layer learns desired
feature patterns from the pairwise ranking loss. The kernels transfer the
feature patterns into soft-match targets at each similarity level and enforce
them on the translation matrix. The word embeddings are tuned accordingly so
that they can produce the desired soft matches. Experiments on a commercial
search engine's query log demonstrate the improvements of K-NRM over prior
feature-based and neural-based states-of-the-art, and explain the source of
K-NRM's advantage: Its kernel-guided embedding encodes a similarity metric
tailored for matching query words to document words, and provides effective
multi-level soft matches
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
This paper provides a unified account of two schools of thinking in
information retrieval modelling: the generative retrieval focusing on
predicting relevant documents given a query, and the discriminative retrieval
focusing on predicting relevancy given a query-document pair. We propose a game
theoretical minimax game to iteratively optimise both models. On one hand, the
discriminative model, aiming to mine signals from labelled and unlabelled data,
provides guidance to train the generative model towards fitting the underlying
relevance distribution over documents given the query. On the other hand, the
generative model, acting as an attacker to the current discriminative model,
generates difficult examples for the discriminative model in an adversarial way
by minimising its discrimination objective. With the competition between these
two models, we show that the unified framework takes advantage of both schools
of thinking: (i) the generative model learns to fit the relevance distribution
over documents via the signals from the discriminative model, and (ii) the
discriminative model is able to exploit the unlabelled data selected by the
generative model to achieve a better estimation for document ranking. Our
experimental results have demonstrated significant performance gains as much as
23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of
applications including web search, item recommendation, and question answering.Comment: 12 pages; appendix adde
Information Retrieval: Recent Advances and Beyond
In this paper, we provide a detailed overview of the models used for
information retrieval in the first and second stages of the typical processing
chain. We discuss the current state-of-the-art models, including methods based
on terms, semantic retrieval, and neural. Additionally, we delve into the key
topics related to the learning process of these models. This way, this survey
offers a comprehensive understanding of the field and is of interest for for
researchers and practitioners entering/working in the information retrieval
domain
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