Videos
See AI2's full collection of videos on our YouTube channel.Viewing 241-250 of 258 videos
Spectral Probabilistic Modeling and Applications to Natural Language Processing
March 3, 2015 | Ankur ParikhBeing able to effectively model latent structure in data is a key challenge in modern AI research, particularly in Natural Language Processing (NLP) where it is crucial to discover and leverage syntactic and semantic relationships that may not be explicitly annotated in the training set. Unfortunately, while…
Multimodal Science Learning
February 26, 2015 | Ken ForbusCreating systems that can work with people, using natural modalities, as apprentices is a key step towards human-level AI. This talk will describe how my group is combining research on sketch understanding, natural language understanding, and analogical learning within the Companion cognitive architecture to…
Semi-Supervised Learning In Realistic Settings
February 5, 2015 | Bhavana DalviSemi-supervised learning (SSL) has been widely used over a decade for various tasks -- including knowledge acquisition-- that lack large amount of training data. My research proposes a novel learning scenario in which the system knows a few categories in advance, but the rest of the categories are unanticipated…
Bayesian Case Model — Generative Approach for Case-based Reasoning and Prototype
January 7, 2015 | Been KimI will present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. The BCM learns prototypes, the ``quintessential" observations that best represent…
Event Discovery, Content Models, and Relevance
December 4, 2014 | Aria HaghigiI discuss three problems in applied natural language processing and machine learning: event discovery from distributed discourse, document content models for information extraction, and relevance engineering for a large-scale personalization engine. The first two are information extraction problems over social…
Toward Scene Understanding
December 3, 2014 | Roozbeh MottaghiScene understanding is one of the holy grails of computer vision, and despite decades of research, it is still considered an unsolved problem. In this talk, I will present a number of methods, which help us take a step further towards the ultimate goal of holistic scene understanding. In particular, I will talk…
Open and Exploratory Extraction of Relations (and Common Sense) from Large Text Corpora
November 10, 2014 | Alan AkbikThe use of deep syntactic information such as typed dependencies has been shown to be very effective in Information Extraction (IE). Despite this potential, the process of manually creating rule-based information extractors that operate on dependency trees is not intuitive for persons without an extensive NLP…
Deep Natural Language Semantics by Combining Logical and Distributional Methods using Probabilistic Logic
November 4, 2014 | Raymond MooneyTraditional logical approaches to semantics and newer distributional or vector space approaches have complementary strengths and weaknesses.We have developed methods that integrate logical and distributional models by using a CCG-based parser to produce a detailed logical form for each sentence, and combining the…
Large-Scale Paraphrasing for Natural Language Generation
October 1, 2014 | Chris Callison-BurchI will present my method for learning paraphrases - pairs of English expressions with equivalent meaning - from bilingual parallel corpora, which are more commonly used to train statistical machine translation systems. My method equates pairs of English phrases like --thrown into jail, imprisoned-- when they…
Modeling Biological Processes for Reading Comprehension
August 5, 2014 | Jonathan BerantMachine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this talk, I will focus on a new reading comprehension task that requires complex reasoning over a single document. The input is a paragraph describing a…