Farzana Rashid


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Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success
Farzana Rashid | Tommaso Fornaciari | Dirk Hovy | Eduardo Blanco | Fernando Vega-Redondo
Findings of the Association for Computational Linguistics: EMNLP 2020

When interacting with each other, we motivate, advise, inform, show love or power towards our peers. However, the way we interact may also hold some indication on how successful we are, as people often try to help each other to achieve their goals. We study the chat interactions of thousands of aspiring entrepreneurs who discuss and develop business models. We manually annotate a set of about 5,500 chat interactions with four dimensions of interaction styles (motivation, cooperation, equality, advice). We find that these styles can be reliably predicted, and that the communication styles can be used to predict a number of indices of business success. Our findings indicate that successful communicators are also successful in other domains.


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Characterizing Interactions and Relationships between People
Farzana Rashid | Eduardo Blanco
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

This paper presents a set of dimensions to characterize the association between two people. We distinguish between interactions (when somebody refers to somebody in a conversation) and relationships (a sequence of interactions). We work with dialogue scripts from the TV show Friends, and do not impose any restrictions on the interactions and relationships. We introduce and analyze a new corpus, and present experimental results showing that the task can be automated.


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Dimensions of Interpersonal Relationships: Corpus and Experiments
Farzana Rashid | Eduardo Blanco
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper presents a corpus and experiments to determine dimensions of interpersonal relationships. We define a set of dimensions heavily inspired by work in social science. We create a corpus by retrieving pairs of people, and then annotating dimensions for their relationships. A corpus analysis shows that dimensions can be annotated reliably. Experimental results show that given a pair of people, values to dimensions can be assigned automatically.