Taraneh Khazaei

Corpus-based analysis of rhetorical relations: A study of lexical cues

In spite of the long tradition of Rhetorical Structure Theory (RST) in computational linguistics, there is no robust method capable of detecting rhetorical relations in the text of discourse. To pave the way for development of such techniques, we carried out experiments aimed at understanding the effectiveness of using corpus-based lexical cues in the identification of RST relations for three different relations and across two different text genres. In particular, we focused on the three relations of CIRCUMSTANCE, EVALUATION, and ELABORATION and two different corpora: newspaper articles and online reviews. The analysis results indicate that the cue-based approaches can be quite effective in detecting CIRCUMSTANCE. However, the ability of lexical cues in relation identification is limited for ELABORATION. For the EVALUATION relation, genre-specific factors can play a more significant role.

Detecting privacy preferences from online social footprints: a literature review

Providing personalized content can be of great value to both users and vendors. However, effective personalization hinges on collecting large amounts of personal data about users. With the exponential growth of activities in social networking websites, they have become a prominent platform to gather and analyze such information. Even though there exist a considerable number of social media users with publicly available data, previous studies have revealed a dichotomy between privacy-related intentions and behaviours. Users often face difficulties specifying privacy policies that are consistent with their actual privacy concerns and attitudes, and simply follow the default permissive privacy setting. Therefore, despite the availability of data, it is imperative to develop and employ algorithms to automatically predict users’ privacy preferences for personalization purposes. In this document, we review prior studies that tackle this challenging task and make use of users’ online social footprints to discover their desired privacy settings.

ProjectTales: Reusing Change Decisions and Rationales in Project Management

Changes are inevitable in project management and project managers are often required to make change decisions that may have significant effects on the success of the project. To support project managers’ decision-making process in such common cases, we have designed and developed a tool called ProjectTales. This tool takes advantage of the valuable information buried in the history of projects and provides various visual and interactive representations of the previous changes. Using ProjectTales, project managers can explore the history of projects, find the change situations similar to their current one, interpret the impact of the change decision, and potentially reuse the decision and the rationale of the change. We are currently planning a user evaluation to compare our tool with a baseline system.

Identification and Disambiguation of Lexical Cues of Rhetorical Relations across Different Text Genres

Lexical cues are linguistic expressions that can signal the presence of a rhetorical relation. However, such cues can be ambiguous as they may signal more than one relation or may not always function as a relation indicator. In this study, we first conduct a corpus-based analysis to derive a set of n-grams as potential lexical cues. These cues are then utilized in graph-based probabilistic models to determine the syntactic context in which the cue is signaling the presence of a particular relation. Evaluation results are reported for various cues of the CIRCUMSTANCE relation, confirming the value of syntactic features for the task of cue disambiguation in the context of Rhetorical Structure Theory. Moreover, using a graph to encode syntactic information is shown to be a more generalizable and effective approach compared to the direct usage of syntactic features.

Student travel award recipients for the 2014 AAAI/SIGAI doctoral consortium

AAAI and SIGAI annually co-organize the AAAI/ SIGAI Doctoral Consortium. The Doctoral Consortium (DC) provides an opportunity for a group of Ph.D. students to discuss and explore their research interests and career objectives with a panel of established researchers in artificial intelligence. SIGAI provides travel funding to support students from institutions outside the U.S., while the NSF provides funding for U.S. students. This year, SIGAI supported four international students to attend and present at the 19th AAAI/SIGAI Doctoral Consortium. These students provided testimonials reporting on their experiences at the DC.