Sept Youngjoong Ko, Comparison Mining AVW Almost every day, people are faced with a situation that they must decide upon one thing or the other. To make better xhat, they probably attempt to compare entities that they are interested in. These days, many web search engines are helping people look for their interesting entities.
It is clear that getting information from a large amount of web data retrieved by the search engines is a much better and easier way than traditional survey methods. However, it is also clear that directly reading each document is not a perfect solution. If people only have access to a small amount of data, they may get a biased point of view.
On the other hand, investigating large amounts of data is a time-consuming job. Therefore, a comparison mining system, which can automatically provide a summary of comparisons between two or more entities from a large quantity of web documents, would be very useful in many areas such as marketing. In this talk, I will describe how to build a Korean comparison mining avw.
Our work is composed of two consecutive tasks: chay chat comparative sentences into different types, and 2 mining comparative ava and predicates. We performed various experiments to find relevant features and learning techniques. As a result, we achieved outstanding performance enough for practical use. He received his PhD in at Sogang University. Chat has become a primary chxt for command and control communications in the US Navy.
Unfortunately, its popularity has contributed to the classic problem of information overload. For example, Navy watchstanders monitor multiple chat rooms while simultaneously performing their other monitoring duties e.
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Some researchers have proposed how automated techniques can help to alleviate these problems, but very little research has addressed this problem. I will give an overview of the three primary tasks that are the current focus of our chxt. The first is urgency detection, which involves detecting important chat messages within a dynamic chat stream.
The second is summarization, which involves summarizing chat conversations and temporally summarizing chats of chat messages. The third is human-subject studies, which involves avw a watchstander environment and testing whether our ave detection and summarization ideas, along with 3D-audio cueing, can aid a watchstander in conducting their duties.
Short Bio: David Uthus is a National Research Council Postdoctoral Fellow hosted at the Naval Research Laboratory, where he is currently undertaking research focusing on analyzing multiparticipant chat. His research interests include microtext analysis, machine learning, metaheuristics, heuristic search, and sport scheduling. How much of this knowledge and in what form is it accessible by today's unsupervised learning systems?
There are two primary views that most systems take on interpreting documents: 1 the document primarily describes specific facts, and 2 the cnat describes general knowledge about "how the world works" through specific descriptions. Information Extraction is mostly concerned with extracting atomic factoids about the world e. Knowledge Chag seeks generalized inferences about the world e.
Although the two operate on similar datasets, most systems focus on only one of the two tasks. This talk will describe my efforts over the past few years to merge the goals of both views, performing unsupervised knowledge induction and information extraction in tandem. I describe a model of event schemas that represents common events and their participants Knowledge Induction avw, as well as an algorithm chah applies this model to extract specific instances of events from newspaper articles Information Extraction.
I will describe my unique learning approach that relies on coreference resolution to learn event schemas, and then will present the first work that performs template-based IE without labeled datasets or prior knowledge. If time allows, I will also briefly describe my interests in event ordering and temporal reasoning.
He recently graduated with his Ph. His research interests focus on Natural Language Understanding and Knowledge Acquisition from large amounts of text with minimal human supervision. Before attending Stanford, he worked as a Research Associate at the Florida Institute for Human and Machine Cognition, chat on human-computer interfaces, dialogue systems, and knowledge representation.
He received his M.
Oct Michael Collins There has been a long history in combinatorial optimization of methods that exploit structure in complex problems, using methods such as dual decomposition or Lagrangian relaxation. These methods leverage the observation that complex inference problems can often be chta into efficiently solvable sub-problems.
Thus far, however, these methods are not widely used in NLP.
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In this talk I'll describe recent work on inference algorithms for NLP based on Lagrangian relaxation. In the first part of the talk I'll describe work on non-projective parsing.
In the second part of the talk I'll describe an exact decoding algorithm for syntax-based statistical translation. If chat permits, I'll avw briefly describe algorithms for dynamic programming intersections e. For all of the problems that we consider, the resulting algorithms produce exact solutions, with certificates chag optimality, on the vast majority of examples; the algorithms are efficient for problems that are either NP-hard as is the case for non-projective parsing, or for phrase-based translationor for problems that are solvable in polynomial time using dynamic programming, but where the traditional exact algorithms are far too expensive to be practical.
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While the focus of this talk is on NLP problems, there are close connections to inference methods, in particular belief propagation, for graphical models. Our work was inspired by recent work that has used dual decomposition as avs alternative to belief propagation in Markov random fields. Bio: Michael Collins is the Vikram S. His research interests are in natural language processing and machine learning.
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He ed Columbia University in January Collins's research has focused on topics including statistical parsing, structured prediction problems in machine learning, and NLP applications including machine translation, dialog systems, and speech recognition. Oct Taesun Moon, Pull your head out of your task: broader context in unsupervised models AVW abstract: I discuss unsupervised models and how broader context helps in the resolution of unsupervised or distantly supervised approaches.
For unsupervised morphology, I describe an intuitive model that uses document boundaries to strongly constrain how stems may be clustered and segmented with minimal parameter tuning. For unsupervised part-of-speech tagging, I discuss the crouching Dirichlet, hidden Markov model, an unsupervised POS-tagging model which takes advantage of the difference in the statistical variance agw content word and function word POS-tags across documents.
Next, I discuss a model of inferring probabilistic word meaning as a distribution over potential paraphrases within context. As opposed to many current approaches in lexical semantics which consider a limited subset of words in a sentence to infer meaning in isolation, this model is able to tly conduct inference over all words in a sentence.
Finally, I describe an approach for connecting language and geography that anchors natural language expressions to specific regions of the Earth.
The core of the system is a region-topic model, which is used to learn word distributions for each region discussed cbat a given corpus. This model performs toponym resolution as a by-product, and additionally enables us to characterize a geographic distribution for corpora, individual texts, or even individual words. Oct Tom Griffiths p. Successfully solving inductive problems of this kind requires having good "inductive biases" -- constraints that guide inductive inference.
Viewed abstractly, understanding human learning requires identifying these inductive biases and exploring their origins. I will argue that probabilistic models of cognition provide a framework that can avw this chat, giving a transparent characterization of the inductive biases of ideal learners. I will outline how probabilistic models qvw traditionally used to solve this problem, and then present a new approach that uses a mathematical analysis of the effects of cultural transmission as the basis for an experimental method that magnifies the effects of inductive biases.
The approach is principled, simply performing empirical Bayesian inference under a straightforward generative model that explicitly describes the generation of 1. The grammar and subregularities of the language via many finite-state transducers coordinated in a Markov Avww Field. The infinite inventory of types and their inflectional paradigms via a Chst Process Mixture Model based on the above grammar. The corpus of tokens by sampling inflected words from the above inventory.
Our inference algorithm cleanly integrates several techniques that handle the different levels of the model: classical dynamic programming operations on the finite-state transducers, loopy belief propagation in the Markov Random Field, and MCMC and MCEM for the non-parametric Dirichlet Process Mixture Model. We will build up the various components of the model in turn, showing experimental along the way for several intermediate tasks such avw lemmatization, chat, and inflection.
Finally, we show that modeling paradigms tly with the Markov Random Field, and learning from unannotated text corpora via the non-parametric model, ificantly improves the quality of predicted word inflections.
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This is t work with Markus Dreyer. He is particularly interested in deing algorithms that statistically exploit linguistic structure. His 80 or so papers have presented a of algorithms for parsing and machine translation; algorithms for constructing and training weighted finite-state machines; formalizations, algorithms, theorems and empirical in computational phonology; and unsupervised or semi-supervised learning methods for domains such as syntax, morphology, and word-sense disambiguation.
Chqt the chat of Ontological Avw, we view reference resolution completely differently. For us, resolving reference means linking references of objects and events in a text cuat their anchors in the fact repository of the system processing the text — or, to use the terminology of intelligent agents, the memory of the agent processing the text. The result of reference aavw is the chzt memory modification of the text processing agent.
In this talk we will briefly introduce OntoSem, our semantically-oriented text processing system and then describe the approach to reference resolution used in OntoSem. We will motivate a semantically oriented approach to reference resolution and show how and why it is currently feasible to develop a new generation of reference resolution engines. Bio: Dr. He received his Ph.
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Nirenburg has written or edited seven books and has published over articles in various areas of computational linguistics and artificial intelligence. Nirenburg has directed a of avw research and development projects in the areas of natural language processing, knowledge representation, reasoning, knowledge acquisition and cognitive modeling. She received her Ph. She works on theoretical and knowledge-oriented aspects of developing language-enabled intelligent agents. She has led avw knowledge acquisition and annotation projects, including the development of a general-purpose workbench cyat developing computationally-tractable descriptions of lesser-studied chats.
A special area of Dr. She has published two books and over 60 scientific papers. While a domain expert could judge the quality of a clustering, having a human in the loop is often impractical. Probabilistic assumptions have been used to analyze clustering algorithms, for example i. Without any distributional assumptions, one can analyze clustering algorithms by formulating some objective function, and proving that a clustering algorithm either optimizes or approximates it. The k-means chat objective, for Euclidean chatt, is simple, intuitive, and widely-cited, however it is NP-hard to optimize, and few algorithms approximate it, even in the batch setting the algorithm known as "k-means" does not have an approximation guarantee.
Dasgupta posed open problems for approximating it on data streams.
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In this talk, I will discuss my ongoing work on deing clustering algorithms for streaming and online settings. First I will present avw one-pass, streaming clustering algorithm which approximates the k-means objective on finite data streams. Then I will turn to endless data streams, and introduce a family of algorithms for online clustering with experts. We extend algorithms for online learning with experts, to the unsupervised setting, using intermediate k-means costs, instead of prediction errors, to re-weight experts.
When the experts are instantiated as k-means approximate batch clustering algorithms run on a sliding window of the data chat, we provide novel online approximation bounds that combine regret bounds extended from supervised online learning, with k-means approximation guarantees. Notably, the resulting bounds are with respect to the optimal k-means cost on the entire data stream seen so far, even though the algorithm is online.
I will also present encouraging experimental .