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Web table extraction, retrieval, and augmentation: A survey
Tables are powerful and popular tools for organizing and manipulating data. A vast number
of tables can be found on the Web, which represent a valuable knowledge resource. The …
of tables can be found on the Web, which represent a valuable knowledge resource. The …
User simulation for evaluating information access systems
With the emergence of various information access systems exhibiting increasing complexity,
there is a critical need for sound and scalable means of automatic evaluation. To address …
there is a critical need for sound and scalable means of automatic evaluation. To address …
Unbiased learning to rank with unbiased propensity estimation
Learning to rank with biased click data is a well-known challenge. A variety of methods has
been explored to debias click data for learning to rank such as click models, result …
been explored to debias click data for learning to rank such as click models, result …
Learning to rank with selection bias in personal search
Click-through data has proven to be a critical resource for improving search ranking quality.
Though a large amount of click data can be easily collected by search engines, various …
Though a large amount of click data can be easily collected by search engines, various …
Deeprank: A new deep architecture for relevance ranking in information retrieval
This paper concerns a deep learning approach to relevance ranking in information retrieval
(IR). Existing deep IR models such as DSSM and CDSSM directly apply neural networks to …
(IR). Existing deep IR models such as DSSM and CDSSM directly apply neural networks to …
Yahoo! learning to rank challenge overview
Learning to rank for information retrieval has gained a lot of interest in the recent years but
there is a lack for large real-world datasets to benchmark algorithms. That led us to publicly …
there is a lack for large real-world datasets to benchmark algorithms. That led us to publicly …
[کتاب][B] Learning to rank for information retrieval and natural language processing
H Li - 2014 - books.google.com
Learning to rank refers to machine learning techniques for training a model in a ranking task.
Learning to rank is useful for many applications in information retrieval, natural language …
Learning to rank is useful for many applications in information retrieval, natural language …
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 …
with a ranked list of tables. This task is not only interesting on its own account, but is also …
Search personalization using machine learning
Firms typically use query-based search to help consumers find information/products on their
websites. We consider the problem of optimally ranking a set of results shown in response to …
websites. We consider the problem of optimally ranking a set of results shown in response to …
The ordinal nature of emotions: An emerging approach
Computational representation of everyday emotional states is a challenging task and,
arguably, one of the most fundamental for affective computing. Standard practice in emotion …
arguably, one of the most fundamental for affective computing. Standard practice in emotion …