CHARM: Inferring Personal Attributes from Conversations

no code implementations • EMNLP 2020 • Anna Tigunova, Andrew Yates, Paramita Mirza, Gerhard Weikum

Personal knowledge about users{'} professions, hobbies, favorite food, and travel preferences, among others, is a valuable asset for individualized AI, such as recommenders or chatbots.

Attribute Keyword Extraction +2

PRIDE: Predicting Relationships in Conversations

no code implementations • EMNLP 2021 • Anna Tigunova, Paramita Mirza, Andrew Yates, Gerhard Weikum

Automatically extracting interpersonal relationships of conversation interlocutors can enrich personal knowledge bases to enhance personalized search, recommenders and chatbots.

Recommendations by Concise User Profiles from Review Text

no code implementations • 2 Nov 2023 • Ghazaleh Haratinezhad Torbati, Anna Tigunova, Andrew Yates, Gerhard Weikum

Recommender systems are most successful for popular items and users with ample interactions (likes, ratings etc.).

Recommendation Systems Representation Learning

RedDust: a Large Reusable Dataset of Reddit User Traits

no code implementations • LREC 2020 • Anna Tigunova, Paramita Mirza, Andrew Yates, Gerhard Weikum

To the best of our knowledge, RedDust is the first annotated language resource about Reddit users at large scale.

Attribute

Listening between the Lines: Learning Personal Attributes from Conversations

1 code implementation • 24 Apr 2019 • Anna Tigunova, Andrew Yates, Paramita Mirza, Gerhard Weikum

Open-domain dialogue agents must be able to converse about many topics while incorporating knowledge about the user into the conversation.

Attribute