Conferences, Lectures, & Seminars
Events for July
-
NL Seminar-Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
Fri, Jul 07, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Amir Hossein Yazdavar, Wright State University
Talk Title: Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
Series: Natural Language Seminar
Abstract: With the rise of social media, millions of people express their moods, feelings and daily struggles with mental health issues routinely on social media platforms like Twitter. Un like traditional observational cohort studies conducted through questionnaires and self reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential of detecting clinical depression symptoms which emulate the PHQ9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.
Biography: Amir is a 2nd year PhD Researcher at Knoesis Center Wright State University, OH under the guidance of Prof. Amit P. Sheth, the founder and executive director of Knoesis Center. He is broadly interested in machine learning incl. deep learning and semantic web incl. creation and use of knowledge graphs and their applications to NLP NLU and social media analytics. He has a particular interest in the extraction of subjective information with applications to search, social and biomedical health applications. At Knoesis Center, he is working on several real world projects mainly focused on studying human behavior on the web via Natural Language Understanding, Social Media Analytics utilizing Machine learning Deep learning and Knowledge Graph techniques. In particular, his focus is to enhance statistical models via domain semantics and guidance from offline behavioral knowledge to understand users behavior from unstructured and large scale Social data.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 6th Flr Conf Rm -# 689
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
NL Seminar-Parsing Graphs with Regular Graph Grammars
Fri, Jul 14, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Sorcha Gilroy, University of Edinburgh
Talk Title: Parsing Graphs with Regular Graph Grammars
Series: Natural Language Seminar
Abstract: Recently, several datasets have become available which represent natural language phenomena as graphs. Hyperedge Replacement Languages HRL have been the focus of much attention as a formalism to represent the graphs in these datasets. Chiang et al. 2013 prove that HRL graphs can be parsed in polynomial time with respect to the size of the input graph. We believe that HRL may be more expressive than is necessary to represent semantic graphs and we propose looking at Regular Graph Languages RGL Courcelle, 1991, which is a subfamily of HRL, as a possible alternative. We provide a top down parsing algorithm for RGL that runs in time linear in the size of the input graph.
Biography: Sorcha is a 2nd year PhD student at the University of Edinburgh and is a student in the Center for Doctoral Training in Data Science. Her PhD is focused on formal languages of graphs for NLP and her supervisors are Adam Lopez and Sebastian Maneth. She completed her undergraduate degree in mathematical sciences at University College Cork and her masters degree in data science at the University of Edinburgh. She is at ISI as an intern in the NLP group.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Webcast: http://webcastermshd.isi.edu/Mediasite/Play/c523b7ef95b443e8b29cfac3092e00081dLocation: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
WebCast Link: http://webcastermshd.isi.edu/Mediasite/Play/c523b7ef95b443e8b29cfac3092e00081d
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
NL Seminar- Neural Sequence Models: Interpretation and Augmentation
Fri, Jul 21, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Xing Shi, USC/ISI
Talk Title: Neural Sequence Models: Interpretation and Augmentation
Series: Natural Language Seminar
Abstract: Recurrent neural networks RNN have been successfully applied to various Natural Language Processing tasks, including language modeling, machine translation, text generation, etc. However, several obstacles still stand in the way: First, due to the RNN's distributional nature, few interpretations of its internal mechanism are obtained, and it remains a black box. Second, because of the large vocabulary sets involved, the text generation is very time consuming. Third, there is no flexible way to constrain the generation of the sequence model with external knowledge. Last, huge training data must be collected to guarantee the performance of these neural models, whereas annotated data such as parallel data used in machine translation are expensive to obtain. This work aims to address the four challenges mentioned above.
To further understand the internal mechanism of the RNN, I choose neural machine translation NMT systems as a testbed. I first investigate how NMT outputs target strings of appropriate lengths, locating a collection of hidden units that learns to explicitly implement this functionality. Then I investigate whether NMT systems learn source language syntax as a by product of training on string pairs. I find that both local and global syntactic information about source sentences is captured by the encoder. Different types of syntax are stored in different layers, with different concentration degrees.
To speed up text generation, I proposed two novel GPU-based algorithms. 1 Utilize the source/target words alignment information to shrink the target side run-time vocabulary. 2 Apply locality sensitive hashing to find nearest word embeddings. Both methods lead to a 2-3x speedup on four translation tasks without hurting machine translation accuracy as measured by BLEU. Furthermore, I integrate a finite state acceptor into the neural sequence model during generation, providing a flexible way to constrain the output, and I successfully apply this to poem generation, in order to control the pentameter and rhyme.
Based on above success, I propose to work on the following. 1 Go one further step towards interpretation: find unit feature mappings, learn the unit temporal behavior, and understand different hyper-parameter settings. 2 Improve NMT performance on low-resource language pairs by fusing an external language model, feeding explicit target-side syntax and utilizing better word embeddings.
Biography: Xing Shi is a PhD student at ISI working with Prof. Kevin Knight.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
