3,780 research outputs found
Reply With: Proactive Recommendation of Email Attachments
Email responses often contain items-such as a file or a hyperlink to an
external document-that are attached to or included inline in the body of the
message. Analysis of an enterprise email corpus reveals that 35% of the time
when users include these items as part of their response, the attachable item
is already present in their inbox or sent folder. A modern email client can
proactively retrieve relevant attachable items from the user's past emails
based on the context of the current conversation, and recommend them for
inclusion, to reduce the time and effort involved in composing the response. In
this paper, we propose a weakly supervised learning framework for recommending
attachable items to the user. As email search systems are commonly available,
we constrain the recommendation task to formulating effective search queries
from the context of the conversations. The query is submitted to an existing IR
system to retrieve relevant items for attachment. We also present a novel
strategy for generating labels from an email corpus---without the need for
manual annotations---that can be used to train and evaluate the query
formulation model. In addition, we describe a deep convolutional neural network
that demonstrates satisfactory performance on this query formulation task when
evaluated on the publicly available Avocado dataset and a proprietary dataset
of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on
Information and Knowledge Management. 201
Mining question-answer pairs from web forum: a survey of challenges and resolutions
Internet forums, which are also known as discussion boards, are popular web applications. Members of the board discuss issues and share ideas to form a community within the board, and as a result generate huge amount of content on different topics on daily basis. Interest in information extraction and knowledge discovery from such sources has been on the increase in the research community. A number of factors are limiting the potentiality of mining knowledge from forums. Lexical chasm or lexical gap that renders some Natural Language Processing techniques (NLP) less effective, Informal tone that creates noisy data, drifting of discussion topic that prevents focused mining and asynchronous issue that makes it difficult to establish post-reply relationship are some of the problems that need to be addressed. This survey introduces these challenges within the framework of question answering. The survey provides description of the problems; cites and explores useful publications to the reader for further examination; provides an overview of resolution strategies and findings relevant to the challenges
What is SemEval evaluating?: A Systematic Analysis of Evaluation Campaigns in NLP
SemEval is the primary venue in the NLP community for the proposal of new
challenges and for the systematic empirical evaluation of NLP systems. This
paper provides a systematic quantitative analysis of SemEval aiming to evidence
the patterns of the contributions behind SemEval. By understanding the
distribution of task types, metrics, architectures, participation and citations
over time we aim to answer the question on what is being evaluated by SemEval.Comment: 12 pages, 6 figure
Overcoming Catastrophic Forgetting by XAI
Explaining the behaviors of deep neural networks, usually considered as black
boxes, is critical especially when they are now being adopted over diverse
aspects of human life. Taking the advantages of interpretable machine learning
(interpretable ML), this work proposes a novel tool called Catastrophic
Forgetting Dissector (or CFD) to explain catastrophic forgetting in continual
learning settings. We also introduce a new method called Critical Freezing
based on the observations of our tool. Experiments on ResNet articulate how
catastrophic forgetting happens, particularly showing which components of this
famous network are forgetting. Our new continual learning algorithm defeats
various recent techniques by a significant margin, proving the capability of
the investigation. Critical freezing not only attacks catastrophic forgetting
but also exposes explainability.Comment: Master of Science Thesis at KAIST; 24 pages; Keywords: continual
learning, catastrophic forgetting, XAI, attribution map, interpretabilit
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