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Michael Gamon

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2018 Conference Paper

Actionable Email Intent Modeling With Reparametrized RNNs

Chu-Cheng Lin
Dongyeop Kang
Michael Gamon
Patrick Pantel

Emails in the workplace are often intentional calls to action for its recipients. We propose to annotate these emails for what action its recipient will take. We argue that our approach of action-based annotation is more scalable and theory-agnostic than traditional speech-act-based email intent annotation, while still carrying important semantic and pragmatic information. We show that our action-based annotation scheme achieves good inter-annotator agreement. We also show that we can leverage threaded messages from other domains, which exhibit comparable intents in their conversation, with domain adaptive Rainbow(Recurrently AttentIve Neural Bag-Of-Words). On a collection of datasets consisting of IRC, Reddit, and email, our reparametrized RNNs outperform common multitask/multidomain approaches on several speech act related tasks. We also experiment with a minimally supervised scenario of email recipient action classiﬁcation, and ﬁnd the reparametrized RNNs learn a useful representation.

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AAAI Conference 2010 Conference Paper

Predicting the Importance of Newsfeed Posts and Social Network Friends

Tim Paek
Michael Gamon
Scott Counts
David Chickering
Aman Dhesi

As users of social networking websites expand their network of friends, they are often flooded with newsfeed posts and status updates, most of which they consider to be understand how people judge the importance of their newsfeed, we conducted a study in which Facebook users were asked to rate the importance of their newsfeed posts as well as their friends. We learned classifiers of newsfeed and friend importance to identify predictive sets of features related to social media properties, the message text, and shared background information. For classifying friend importance, the best performing model achieved 85% accuracy and 25% error reduction. By leveraging this model for classifying newsfeed posts, the best newsfeed classifier achieved 64% accuracy and 27% error reduction.

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