Mihalcea, Rada



Cultural Influences on the Measurement of Personal Values through Words

AAAI Conferences

Texts posted on the web by users from diverse cultures provide a nearly endless source of data that researchers can use to study human thoughts and language patterns. However, unless care is taken to avoid it, models may be developed in one cultural setting and deployed in another, leading to unforeseen consequences. We explore the effects of using models built from a corpus of texts from multiple cultures in order to learn about each represented people group separately. To do this, we employ a topic modeling approach to quantify open-ended writing responses describing personal values and everyday behaviors in two distinct cultures. We show that some topics are more prominent in one culture compared to the other, while other topics are mentioned to similar degrees. Furthermore, our results indicate that culture influences how value-behavior relationships are exhibited. While some relationships exist in both cultural groups, in most cases we see that the observed relations are dependent on the cultural background of the data set under examination.


Left-Handed or Right-Handed? A Data-Driven Approach to Analysing Characteristics of Handedness Based on Language Use

AAAI Conferences

Numerous studies have identified differences between left-handed and right-handed people, especially in the fields of psychology and neuroscience. Using a social media setting, this paper presents a data-driven approach to explore whether a person's handedness can be identified given his or her writing, and shows handedness characteristics that can be inferred from language.


Values in Words: Using Language to Evaluate and Understand Personal Values

AAAI Conferences

People's values provide a decision-making framework that helps guide their everyday actions. Most popular methods of assessing values show tenuous relationships with everyday behaviors. Using a new Amazon Mechanical Turk dataset (N = 767) consisting of people's language, values, and behaviors, we explore the degree to which attaining "ground truth" is possible with regards to such complicated mental phenomena. We then apply our findings to a corpus of Facebook user (N=130,828) status updates in order to understand how core values influence the personal thoughts and behaviors that users share through social media. Our findings suggest that self-report questionnaires for abstract and complex phenomena, such as values, are inadequate for painting an accurate picture of individual mental life. Free response language data and language modeling show greater promise for understanding both the structure and content of concepts such as values and, additionally, exhibit a predictive edge over self-report questionnaires.


What Women Want: Analyzing Research Publications to Understand Gender Preferences in Computer Science

AAAI Conferences

While the number of women who choose to pursue computer science and engineering careers is growing, men continue to largely outnumber them. In this paper, we describe a data mining approach that relies on a large collection of scientific articles to identify differences in gender interests in this field. Our hope is that through a better understanding of the differences between male and female preferences, we can enable more effective outreach and retention, and consequently contribute to the growth of the number of women who choose to pursue careers in this field.


Semantic Relatedness Using Salient Semantic Analysis

AAAI Conferences

This paper introduces a novel method for measuring semantic relatedness using semantic profiles constructed from salient encyclopedic features. The model is built on the notion that the meaning of a word can be characterized by the salient concepts found in its immediate context. In addition to being computationally efficient, the new model has superior performance and remarkable consistency when compared to both knowledge-based and corpus-based state-of-the-art semantic relatedness models.


AAAI 2008 Workshop Reports

AI Magazine

AAAI 2008 Workshop Reports


AAAI 2008 Workshop Reports

AI Magazine

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13–14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers; AI Education Workshop Colloquium; Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging; Human Implications of Human-Robot Interaction; Intelligent Techniques for Web Personalization and Recommender Systems; Metareasoning: Thinking about Thinking; Multidisciplinary Workshop on Advances in Preference Handling; Search in Artificial Intelligence and Robotics; Spatial and Temporal Reasoning; Trading Agent Design and Analysis; Transfer Learning for Complex Tasks; What Went Wrong and Why: Lessons from AI Research and Applications; and Wikipedia and Artificial Intelligence: An Evolving Synergy.


Networks and Natural Language Processing

AI Magazine

Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.


Networks and Natural Language Processing

AI Magazine

Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.