Month: November 2015

Please join us for the next NLP Seminar this Thursday (Dec. 3) at 4pm in 205 South Hall.

Speaker: Vinodkumar Prabhakaran, Stanford (

Title: Social Power in Interactions: Computational Analysis and Detection of Power


Abstract: In this talk, I will present study done as part of my thesis research on how social power relations affect the way people interact with one another and how we can use statistical machine learning techniques to detect these power relations automatically. This study is performed in the domain of organizational emails using the Enron email corpus. I will first present the problem of predicting superior-subordinate relationship between pairs of people, based solely on the language and structure of interactions within single email threads. We found many dialog behavior patterns that are salient to the direction of power. For example, superiors tend to send shorter messages, and use more overt displays of power than subordinates. I will then present the results of our investigation on how the gender of the participants impacts the manifestations of power. For example, do male superiors and female superiors differ in how often they use overt displays of power?

For this week’s meeting, rather than focusing on completed work, those
interested are invited to request feedback on current and future research efforts.

Those working on something now who could benefit from feedback from the seminar, or who have a half-baked idea on some new direction that you want feedback on shaping, plan to talk for about 10 minutes each. Graduate students and undergraduate seminar participants are welcome to participate.

Nov 5: Radu Soricut: Unsupervised Morphology Induction Using Word Embeddings

Please join us for the next NLP Seminar this Thursday (Nov. 5) at 4pm in 205 South Hall.

Speaker: Radu Soricut (Google)

Title: Unsupervised Morphology Induction Using Word Embeddings


We present a language agnostic, unsupervised method for inducing morphological transformations between words. The method relies on certain regularities manifest in high dimensional vector spaces. We show that this method is capable of discovering a wide range of morphological rules, which in turn are used to build morphological analyzers. We evaluate this method across six different languages and nine datasets, and show significant improvements across all languages.

Slides: (pdf)