Two ases of prominent internal possessor onstru tions Sandy Rit hie SOAS, University of London · tr7soas.a .uk

Source URL: nlp.ipipan.waw.pl

• Mesoamerican languages
• Agglutinative languages
• Oromo language
• Rama language
• Tzeltal language

Discourse Parsing: Learning FOL Rules based on Rich Verb Semantic Representations to automatically label Rhetorical Relations Rajen Subba Computer Science University of Illinois Chicago, IL, USA

Source URL: nlp.cs.uic.edu

Syntactic, Semantic and Information Structures of Floating Quantifiers Ryo Otoguro & Liselotte Snijders Waseda University HeadLex16, Warsaw, 28 July 2016

Source URL: nlp.ipipan.waw.pl

• American football in Japan
• Waseda University

Joint Inference for Cross-document Information Extraction Qi Li, Sam Anzaroot, Wen-Pin Lin, Xiang Li, Heng Ji Computer Science Department, Queens College and Graduate Center City University of New York New York City, NY,

Source URL: nlp.cs.rpi.edu

What Kinds of Knowledge Are Needed for Genuine Understanding?        Lenhart  Schubert     University  of  Rochester NLP in the 60’s to mid-80s

Source URL: cognitum.ws

Reception Sunday, June 4 (18:30-21:30) Kimmel Center(New York University) The reception will be held on the 10th floor of the Kimmel Center on Sunday, June:30-21:30 ). The address of the

Source URL: nlp.cs.nyu.edu

How to Be a Successful PhD Student (in Computer Science (in NLP/ML)) Mark Dredze (Johns Hopkins University) Hanna M. Wallach (University of Massachusetts Amherst) Version 1.0, March 20, 2012 Being a graduate student can

Source URL: dirichlet.net

Open Research Online The Open University’s repository of research publications and other research outputs Combining NLP and semantics for mining software technologies from research publications

Source URL: oro.open.ac.uk

Part-of-Speech Tagging from 97% to 100%: Is It Time for Some Linguistics? Christopher D. Manning Departments of Linguistics and Computer Science Stanford University 353 Serra Mall, Stanford CA

Source URL: nlp.stanford.edu

Generating Semantically Precise Scene Graphs from Textual Descriptions for Improved Image Retrieval Sebastian Schuster, Ranjay Krishna, Angel Chang, Li Fei-Fei, and Christopher D. Manning Stanford University, Stanford, C

Source URL: nlp.stanford.edu

• Graph theory
• Computing
• Artificial intelligence
• Computational linguistics
• Mathematics
• Compiler construction
• Parsing
• Knowledge representation
• Scene graph
• DOT
• Content-based image retrieval
• Graph