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.
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.
1 code implementation • 26 Mar 2024 • Paramita Mirza, Viju Sudhi, Soumya Ranjan Sahoo, Sinchana Ramakanth Bhat
State-of-the-art intent classification (IC) and slot filling (SF) methods often rely on data-intensive deep learning models, limiting their practicality for industry applications.
1 code implementation • ACL 2021 • Paramita Mirza, Mostafa Abouhamra, Gerhard Weikum
High-quality alignment between movie scripts and plot summaries is an asset for learning to summarize stories and to generate dialogues.
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.
no code implementations • IJCNLP 2019 • Simon Razniewski, Nitisha Jain, Paramita Mirza, Gerhard Weikum
Scalar implicatures are language features that imply the negation of stronger statements, e. g., {``}She was married twice{''} typically implicates that she was not married thrice.
1 code implementation • IJCNLP 2019 • Filipe Mesquita, Matteo Cannaviccio, Jordan Schmidek, Paramita Mirza, Denilson Barbosa
KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts expressed in natural language text on the web.
no code implementations • WS 2019 • Youmna Ismaeil, Oana Balalau, Paramita Mirza
In this work, we revisit the functions of language proposed by linguist Roman Jakobson and we highlight their potential in analyzing online forum 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.
1 code implementation • 10 Jul 2018 • Paramita Mirza, Simon Razniewski, Fariz Darari, Gerhard Weikum
In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2, 474 frequent relations in Wikidata.
no code implementations • SEMEVAL 2018 • Paramita Mirza, Fariz Darari, Rahmad Mahendra
We present KOI (Knowledge of Incidents), a system that given news articles as input, builds a knowledge graph (KOI-KG) of incidental events.
no code implementations • ACL 2017 • Paramita Mirza, Simon Razniewski, Fariz Darari, Gerhard Weikum
Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award.
no code implementations • COLING 2016 • Paramita Mirza, Sara Tonelli
Temporal relation classification is a challenging task, especially when there are no explicit markers to characterise the relation between temporal entities.
1 code implementation • COLING 2016 • Paramita Mirza, Sara Tonelli
The effects of the interaction between the temporal and the causal components, although limited, yield promising results and confirm the tight connection between the temporal and the causal dimension of texts.
Ranked #1 on Temporal Information Extraction on TimeBank
no code implementations • ACL 2014 • Paramita Mirza
Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events.