Word embeddings, which represent words as dense feature vectors, are widely used in natural language processing. In their seminal paper on word2vec, Mikolov and colleagues showed that a feature space created by training a word prediction network on a large text corpus will encode semantic information that supports analogy by vector arithmetic, e.g., "king" minus "man" plus "woman" equals "queen". To help novices appreciate this idea, people have sought effective graphical representations of word embeddings.We describe a new interactive tool for visually exploring word embeddings. Our tool allows users to define semantic dimensions by specifying opposed word pairs, e.g., gender is defined by pairs such as boy/girl and father/mother, and age by pairs such as father/son and mother/daughter. Words are plotted as points in a zoomable and rotatable 3D space, where the third ”residual” dimension encodes distance from the hyperplane defined by all the opposed word vectors with age and gender subtracted out. Our tool allows users to visualize vector analogies, drawing the vector from “king” to “man” and a parallel vector from “woman” to “king-man+woman”, which is closest to “queen”. Visually browsing the embedding space and experimenting with this tool can make word embeddings more intuitive. We include a series of experiments teachers can use to help K-12 students appreciate the strengths and limitations of this representation.
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Using Graphical Features To Improve Demographic Prediction From Smart Phone Data
Demographic information such as gender, age, ethnicity, level of
education, disabilities, employment, and socio-economic status
are important in the area of social science, survey and marketing.
But it is difficult to obtain the demographic information from
users due to reluctance of users to participate and low response
rate. Through automated demographics prediction from smart
phone sensor data, researchers can obtain this valuable
information in a nonintrusive and cost-effective manner. We
approach the problem of demographic prediction, namely,
classification of gender, age group and job type, through the use
of a graphical feature based framework. The framework
represents information collected from sensor networks as graphs,
extracts useful and relevant graphical features, and predicts
demographic information. We evaluated our approach on the
Nokia Mobile Phone dataset for the three classification tasks:
gender, age-group and job-type. Our approach produced
comparable results with most of the state of the art methods while
having the additional advantage of general applicability to sensor
networks without using sophisticated and application-specific
feature generation techniques, background knowledge and special
techniques to address class imbalance.
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- Award ID(s):
- 1646640
- NSF-PAR ID:
- 10033442
- Date Published:
- Journal Name:
- SIGMOD Workshop on Network Data Analytics (NDA)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Word embeddings, which represent words as dense feature vectors, are widely used in natural language processing. In their seminal paper on word2vec, Mikolov and colleagues showed that a feature space created by training a word prediction network on a large text corpus will encode semantic information that supports analogy by vector arithmetic, e.g., "king" minus "man" plus "woman" equals "queen". To help novices appreciate this idea, people have sought effective graphical representations of word embeddings.We describe a new interactive tool for visually exploring word embeddings. Our tool allows users to define semantic dimensions by specifying opposed word pairs, e.g., gender is defined by pairs such as boy/girl and father/mother, and age by pairs such as father/son and mother/daughter. Words are plotted as points in a zoomable and rotatable 3D space, where the third ”residual” dimension encodes distance from the hyperplane defined by all the opposed word vectors with age and gender subtracted out. Our tool allows users to visualize vector analogies, drawing the vector from “king” to “man” and a parallel vector from “woman” to “king-man+woman”, which is closest to “queen”. Visually browsing the embedding space and experimenting with this tool can make word embeddings more intuitive. We include a series of experiments teachers can use to help K-12 students appreciate the strengths and limitations of this representation.more » « less
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Accepted Manuscript1.0
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