148,345 research outputs found
Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank
Understanding why a model makes certain predictions is crucial when adapting
it for real world decision making. LIME is a popular model-agnostic feature
attribution method for the tasks of classification and regression. However, the
task of learning to rank in information retrieval is more complex in comparison
with either classification or regression. In this work, we extend LIME to
propose Rank-LIME, a model-agnostic, local, post-hoc linear feature attribution
method for the task of learning to rank that generates explanations for ranked
lists.
We employ novel correlation-based perturbations, differentiable ranking loss
functions and introduce new metrics to evaluate ranking based additive feature
attribution models. We compare Rank-LIME with a variety of competing systems,
with models trained on the MS MARCO datasets and observe that Rank-LIME
outperforms existing explanation algorithms in terms of Model Fidelity and
Explain-NDCG. With this we propose one of the first algorithms to generate
additive feature attributions for explaining ranked lists.Comment: 4 pages + reference
Text Classification: A Perspective of Deep Learning Methods
In recent years, with the rapid development of information on the Internet,
the number of complex texts and documents has increased exponentially, which
requires a deeper understanding of deep learning methods in order to accurately
classify texts using deep learning techniques, and thus deep learning methods
have become increasingly important in text classification. Text classification
is a class of tasks that automatically classifies a set of documents into
multiple predefined categories based on their content and subject matter. Thus,
the main goal of text classification is to enable users to extract information
from textual resources and process processes such as retrieval, classification,
and machine learning techniques together in order to classify different
categories. Many new techniques of deep learning have already achieved
excellent results in natural language processing. The success of these learning
algorithms relies on their ability to understand complex models and non-linear
relationships in data. However, finding the right structure, architecture, and
techniques for text classification is a challenge for researchers. This paper
introduces deep learning-based text classification algorithms, including
important steps required for text classification tasks such as feature
extraction, feature reduction, and evaluation strategies and methods. At the
end of the article, different deep learning text classification methods are
compared and summarized
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet
everyday has led to many commercial video search engines, which mainly rely on
text metadata for search. However, metadata is often lacking for user-generated
videos, thus these videos are unsearchable by current search engines.
Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity
problem by directly analyzing the visual and audio streams of each video. CBVR
encompasses multiple research topics, including low-level feature design,
feature fusion, semantic detector training and video search/reranking. We
present novel strategies in these topics to enhance CBVR in both accuracy and
speed under different query inputs, including pure textual queries and query by
video examples. Our proposed strategies have been incorporated into our
submission for the TRECVID 2014 Multimedia Event Detection evaluation, where
our system outperformed other submissions in both text queries and video
example queries, thus demonstrating the effectiveness of our proposed
approaches
Semantic spaces revisited: investigating the performance of auto-annotation and semantic retrieval using semantic spaces
Semantic spaces encode similarity relationships between objects as a function of position in a mathematical space. This paper discusses three different formulations for building semantic spaces which allow the automatic-annotation and semantic retrieval of images. The models discussed in this paper require that the image content be described in the form of a series of visual-terms, rather than as a continuous feature-vector. The paper also discusses how these term-based models compare to the latest state-of-the-art continuous feature models for auto-annotation and retrieval
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