acl acl2013 acl2013-313 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Yoav Artzi ; Nicholas FitzGerald ; Luke Zettlemoyer
Abstract: unkown-abstract
Reference: text
sentIndex sentText sentNum sentScore
1 Semantic Parsing with Combinatory Categorial Grammars Yoav Artzi, Nicholas FitzGerald and Luke Zettlemoyer Computer Science & Engineering University of Washington Seattle, WA 98195 {yoav ,n fit z , l z } @ c s . [sent-1, score-0.043]
2 edu s 1 Abstract Semantic parsers map natural language sentences to formal representations of their underlying meaning. [sent-3, score-0.293]
3 Building accurate semantic parsers without prohibitive engineering costs is a longstanding, open research problem. [sent-4, score-0.431]
4 The tutorial will describe general principles for building semantic parsers. [sent-5, score-0.265]
5 The presentation will be divided into two main parts: modeling and learning. [sent-6, score-0.144]
6 The modeling section will include best practices for grammar design and choice of semantic representation. [sent-7, score-0.298]
7 The discussion will be guided by examples from several domains. [sent-8, score-0.059]
8 To illustrate the choices to be made and show how they can be approached within a real-life representation language, we will use ä˝? [sent-9, score-0.169]
9 In the learning part, we will describe a unified approach for learning Combinatory Categorial Grammar (CCG) semantic parsers, that in- duces both a CCG lexicon and the parameters of a parsing model. [sent-11, score-0.442]
10 The approach learns from data with labeled meaning representations, as well as from more easily gathered weak supervision. [sent-12, score-0.262]
11 It also enables grounded learning where the semantic parser is used in an interactive environment, for example to read and execute instructions. [sent-13, score-0.644]
12 Similarly, the algorithms for inducing CCGs focus on tasks that are formalism independent, learning the meaning of words and estimating parsing parameters. [sent-17, score-0.341]
13 The tutorial will be backed by implementation and experiments in the University of Washington Semantic Parsing Framework (UW SPF). [sent-19, score-0.225]
14 Modeling (a) Questions for database queries (b) Plurality and determiner resolution in grounded applications (c) Event semantics and imperatives in instructional language 3. [sent-23, score-0.579]
15 Learning (a) A unified learning algorithm (b) Learning with supervised data i. [sent-24, score-0.195]
16 Unification-based learning (c) Weakly supervised learning without labeled meaning representations 3 Instructors Yoav Artzi is a Ph. [sent-26, score-0.379]
17 candidate in the Computer Science & Engineering department at the University of Washington. [sent-28, score-0.052]
18 His research studies the acquisition of grounded natural language understanding within interactive systems. [sent-29, score-0.427]
19 His work focuses on modeling semantic representations and designing weakly supervised learning algorithms. [sent-30, score-0.563]
20 His research interests are grounded natural language understanding and generation. [sent-35, score-0.439]
21 He is a recipient of an Intel Science and Technology Center Fellowship and an NSERC Postgraduate Scholarship. [sent-36, score-0.1]
22 Luke Zettlemoyer is an Assistant Professor in the Computer Science & Engineering department at the University of Washington. [sent-37, score-0.052]
23 His research interests are in the intersections of natural language processing, machine learning and decision making under uncertainty. [sent-38, score-0.269]
24 Honors include best paper awards at UAI 2005 and ACL 2009, selection to the DARPA CSSG, and an NSF CAREER Award. [sent-39, score-0.086]
25 Proce Sdoinfiags, B oufl tghear5i a1,st A Aungunusta 4l M-9e 2e0t1in3g. [sent-40, score-0.109]
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