Predicate Informed Syntax Guidance For Semantic Role Labeling

DOWNLOAD

Download Predicate Informed Syntax Guidance For Semantic Role Labeling PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Predicate Informed Syntax Guidance For Semantic Role Labeling book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Predicate Informed Syntax Guidance For Semantic Role Labeling

DOWNLOAD
Author : Sijia Wang
language : en
Publisher:
Release Date : 2020

Predicate Informed Syntax Guidance For Semantic Role Labeling written by Sijia Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electronic dissertations categories.

In this thesis, we consider neural network approaches to the semantic role labeling task in seman-tic parsing. Recent state-of-the-art results for semantic role labeling are achieved by combiningLSTM neural networks and pre-trained features. This work offers a simple BERT-based modelwhich shows that, contrary to the popular belief that more complexity means better performance,removing LSTM improves the state of the art for span-based semantic role labeling. This modelhas improved F1 scores on both the test set of CoNLL-2012, and the Brown test set of CoNLL-2005 by at least 3 percentage points.In addition to this refinement of existing architectures, we also propose a new mechanism. Therehas been an active line of research focusing on incorporating syntax information into the atten-tion mechanism for semantic parsing. However, the existing models do not make use of whichsub-clause a given token belongs to or where the boundary of the sub-clause lies. In this thesis,we propose a predicate-aware attention mechanism that explicitly incorporates the portion of theparsing spanning from the predicate. The proposed Syntax-Guidance (SG) mechanism further improves the model performance. We compare the predicate informed method with three other SG mechanisms in detailed error analysis, showing the advantage and potential research directions ofthe proposed method.

Semantic Role Labeling

DOWNLOAD
Author : Martha Palmer
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2011-02-02

Semantic Role Labeling written by Martha Palmer and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-02 with Computers categories.

This book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard stages and feature choices, as well as giving details of several specific systems. Recent advances include the use of joint inference to take advantage of context sensitivities, and attempts to improve performance by closer integration of the syntactic parsing task with semantic role labeling. Chapter 3 also discusses the impact the granularity of the semantic roles has on system performance. Having outlined the basic approach with respect to English, Chapter 4 goes on to discuss applying the same techniques to other languages, using Chinese as the primary example. Although substantial training data is available for Chinese, this is not the case for many other languages, and techniques for projecting English role labels onto parallel corpora are also presented. Table of Contents: Preface / Semantic Roles / Available Lexical Resources / Machine Learning for Semantic Role Labeling / A Cross-Lingual Perspective / Summary

Verbose Labels For Semantic Roles

DOWNLOAD
Author : Ravikiran Vadlapudi
language : en
Publisher:
Release Date : 2013

Verbose Labels For Semantic Roles written by Ravikiran Vadlapudi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Natural language processing (Computer science) categories.

We introduce a new task that takes the output of semantic role labeling and associates each of the argument slots for a predicate with a verbose description such as buyer or thing_bought to semantic role labels such as 'Arg0' and 'Arg1' for predicate like "buy". Ambiguous verb senses and syntactic alternations make this a challenging task. We adapt the frame information for each verb in the PropBank to create our training data. We propose various baseline methods and more informed models which can identify such verbose labels with 95.2% accuracy if the semantic roles have already been correctly identified. We extend our work to text visualization to illustrate the importance of verbose labeling. As a proof of concept, we built an interactive browser for human history articles from Wikipedia, called lensingWikipedia.

Semantic Role Labeling Using Lexicalized Tree Adjoining Grammars

DOWNLOAD
Author : Yudong Liu
language : en
Publisher:
Release Date : 2009

Semantic Role Labeling Using Lexicalized Tree Adjoining Grammars written by Yudong Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computational linguistics categories.

The predicate-argument structure (PAS) of a natural language sentence is a useful representation that can be used for a deeper analysis of the underlying meaning of the sentence or directly used in various natural language processing (NLP) applications. The task of semantic role labeling (SRL) is to identify the predicate-argument structures and label the relations between the predicate and each of its arguments. Researchers have been studying SRL as a machine learning problem in the past six years, after large-scale semantically annotated corpora such as FrameNet and PropBank were released to the research community. Lexicalized Tree Adjoining Grammars (LTAGs), a tree rewriting formalism, are often a convenient representation for capturing locality of predicate-argument relations. Our work in this thesis is focused on the development and learning of the state of the art discriminative SRL systems with LTAGs. Our contributions to this field include: We apply to the SRL task a variant of the LTAG formalism called LTAG-spinal and the associated LTAG-spinal Treebank (the formalism and the Treebank were created by Libin Shen). Predicate-argument relations that are either implicit or absent from the original Penn Treebank are made explicit and accessible in the LTAG-spinal Treebank, which we show to be a useful resource for SRL. We propose the use of the LTAGs as an important additional source of features for the SRL task. Our experiments show that, compared with the best-known set of features that are used in state of the art SRL systems, LTAG-based features can improve SRL performance significantly. We treat multiple LTAG derivation trees as latent features for SRL and introduce a novel learning framework -- Latent Support Vector Machines (LSVMs) to the SRL task using these latent features. This method significantly outperforms state of the art SRL systems. In addition, we adapt an SRL framework to a real-world ternary relation extraction task in the biomedical domain. Our experiments show that the use of SRL related features significantly improves performance over the system using only shallow word-based features.

Robust Semantic Role Labeling

DOWNLOAD
Author : Yi Szu-Ting
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2015-05-25

Robust Semantic Role Labeling written by Yi Szu-Ting and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-25 with categories.

Correctly identifying semantic entities and successfully disambiguating the relations between them and their predicates is an important and necessary step for successful natural language processing applications, such as text summarization, question answering, and machine translation. Researchers have studied this problem, semantic role labeling (SRL), as a machine learning problem since 2000. However, after using an optimal global inference algorithm to combine several SRL systems, the growth of SRL performance seems to have reached a plateau. Syntactic parsing is the bottleneck of the task of semantic role labeling and robustness is the ultimate goal. In this book, we investigate ways to train a better syntactic parser and increase SRL system robustness. We demonstrate that parse trees augmented by semantic role markups can serve as suitable training data for training a parser for an SRL system. For system robustness, we propose that it is easier to learn a new set of semantic roles. The new roles are less verb- dependent than the original PropBank roles. As a result, the SRL system trained on the new roles achieves significantly better robustness.

The Semantics Of Role Labeling

DOWNLOAD
Author :
language : en
Publisher:
Release Date : 2013

The Semantics Of Role Labeling written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.

Semantic Role Labeling Of Implicit Arguments For Nominal Predicates

DOWNLOAD
Author : Matthew Steven Gerber
language : en
Publisher:
Release Date : 2011

Semantic Role Labeling Of Implicit Arguments For Nominal Predicates written by Matthew Steven Gerber and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Electronic dissertations categories.

Semantic Features For Semantic Role Labeling

DOWNLOAD
Author : Liam R. McGrath
language : en
Publisher:
Release Date : 2011

Semantic Features For Semantic Role Labeling written by Liam R. McGrath and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Semantics categories.

Syntax Driven Argument Identification And Multi Argument Classification For Semantic Role Labeling

DOWNLOAD
Author :
language : en
Publisher:
Release Date : 2007

Syntax Driven Argument Identification And Multi Argument Classification For Semantic Role Labeling written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computational linguistics categories.

Learning Structured Probabilistic Models For Semantic Role Labeling

DOWNLOAD
Author : David Terrell Vickrey
language : en
Publisher:
Release Date : 2010

Learning Structured Probabilistic Models For Semantic Role Labeling written by David Terrell Vickrey and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.

Teaching a computer to read is one of the most interesting and important artificial intelligence tasks. In this thesis, we focus on semantic role labeling (SRL), one important processing step on the road from raw text to a full semantic representation. Given an input sentence and a target verb in that sentence, the SRL task is to label the semantic arguments, or roles, of that verb. For example, in the sentence "Tom eats an apple, " the verb "eat" has two roles, Eater = "Tom" and Thing Eaten = "apple". Most SRL systems, including the ones presented in this thesis, take as input a syntactic analysis built by an automatic syntactic parser. SRL systems rely heavily on path features constructed from the syntactic parse, which capture the syntactic relationship between the target verb and the phrase being classified. However, there are several issues with these path features. First, the path feature does not always contain all relevant information for the SRL task. Second, the space of possible path features is very large, resulting in very sparse features that are hard to learn. In this thesis, we consider two ways of addressing these issues. First, we experiment with a number of variants of the standard syntactic features for SRL. We include a large number of syntactic features suggested by previous work, many of which are designed to reduce sparsity of the path feature. We also suggest several new features, most of which are designed to capture additional information about the sentence not included in the standard path feature. We build an SRL model using the best of these new and old features, and show that this model achieves performance competitive with current state-of-the-art. The second method we consider is a new methodology for SRL based on labeling canonical forms. A canonical form is a representation of a verb and its arguments that is abstracted away from the syntax of the input sentence. For example, "A car hit Bob" and "Bob was hit by a car" have the same canonical form, {Verb = "hit", Deep Subject = "a car", Deep Object = "a car"}. Labeling canonical forms makes it much easier to generalize between sentences with different syntax. To label canonical forms, we first need to automatically extract them given an input parse. We develop a system based on a combination of hand-coded rules and machine learning. This allows us to include a large amount of linguistic knowledge and also have the robustness of a machine learning system. Our system improves significantly over a strong baseline, demonstrating the viability of this new approach to SRL. This latter method involves learning a large, complex probabilistic model. In the model we present, exact learning is tractable, but there are several natural extensions to the model for which exact learning is not possible. This is quite a general issue; in many different application domains, we would like to use probabilistic models that cannot be learned exactly. We propose a new method for learning these kinds of models based on contrastive objectives. The main idea is to learn by comparing only a few possible values of the model, instead of all possible values. This method generalizes a standard learning method, pseudo-likelihood, and is closely related to another, contrastive divergence. Previous work has mostly focused on comparing nearby sets of values; we focus on non-local contrastive objectives, which compare arbitrary sets of values. We prove several theoretical results about our model, showing that contrastive objectives attempt to enforce probability ratio constraints between the compared values. Based on this insight, we suggest several methods for constructing contrastive objectives, including contrastive constraint generation (CCG), a cutting-plane style algorithm that iteratively builds a good contrastive objective based on finding high-scoring values. We evaluate CCG on a machine vision task, showing that it significantly outperforms pseudo-likelihood, contrastive divergence, as well as a state-of-the-art max-margin cutting-plane algorithm.