View Presentation Slides
Abstract
Linguistic relation learning is a pervasive research area in Natural Language Processing, which ranges from syntactic relations captured by syntactic parsers to semantic relations, e.g., modeled with Semantic Role Labeling, coreference resolution, discourse structure approaches or more directly with systems for relation extraction applied to pairs of entities. Such methods typically target constituents spanning one or multiple sentences.

An even more challenging class regards relational learning from pairs of entire (short) texts, which, to be captured, requires the joint analysis of the relations between the different constituents in both texts. Typical examples of such relations are: textual entailment, paraphrasing, correct vs. incorrect association of question with its target answer passage, correct/incorrect translation between a text and its translation, etc.

Given the complexity of providing a theory modeling such relations, researchers rely on machine learning methods. Such models define vector of features for training relational classifiers, which are based on several textual similarities. The latter are computed using different representations, applied to the two texts.

This talk will show a different approach to relational learning from text pairs, which is based on structural kernels: first, a structural/linguistic representation of the text is provided, e.g., using syntactic parsing or semantic role labeling. Then, semantic links between the constituents of the two texts are automatically derived, e.g., using string matching or lexical similarity. Finally, the obtained structures are processed by structural kernels, which automatically map them in feature spaces, where learning algorithms can learn the target relation encoded by the data labels. The talk will show results using different representations for passage reranking in question answering systems.
All Participant Lectures will be held in Room S1, 4th Floor.

Biography
Alessandro Moschitti is a Senior Research Scientist at the Qatar Computing Research Institute (QCRI) and a tenured professor at the Computer Science (CS) Department of the University of Trento, Italy. He obtained his PhD in CS from the University of Rome in 2003. He has been the only non-US faculty member to participate in the IBM Watson Jeopardy! challenge. He has significant expertise in both theoretical and applied ML for NLP, IR and Data Mining. He has devised innovative kernels for advanced syntactic/semantic processing with support vector and other kernel-based machines. He is an author or co-author of more than 190 scientific articles in many different areas of NLP, ranging from Semantic Role Labeling to Opinion Mining. He has been an area chair for the semantics track at ACL and IJCNLP conferences and for machine learning track at ACL and ECML. Additionally, he has been PC chair of other important conferences and workshops for the ML and ACL communities. Currently, he is the General Chair of EMNLP 2014 and he is on the editorial board of JAIR, JNLE and JoDS. He has received three IBM Faculty Awards, one Google Faculty Award and three best paper awards.