Chunk parsing has focused on the recognition of partial constituent structures at the level of individual chunks. Little attention has been paid to the question of how such partial analyses can be combined into larger structures for complete utterances. Such larger structures are not only desirable for a deeper syntactic analysis. They also constitute a necessary prerequisite for assigning function-argument structure.
Maintaining high annotation consistency in large corpora is crucial for statistical learning; however, such work is hard, especially for tasks containing semantic elements. This paper describes predicate argument structure analysis using transformation-based learning. An advantage of transformation-based learning is the readability of learned rules.
Predicate-argument structure contains rich semantic information of which statistical machine translation hasn’t taken full advantage. In this paper, we propose two discriminative, feature-based models to exploit predicateargument structures for statistical machine translation: 1) a predicate translation model and 2) an argument reordering model.
This paper presents a predicate-argument structure analysis that simultaneously conducts zero-anaphora resolution. By adding noun phrases as candidate arguments that are not only in the sentence of the target predicate but also outside of the sentence, our analyzer identiﬁes arguments regardless of whether they appear in the sentence or not. Because we adopt discriminative models based on maximum entropy for argument identiﬁcation, we can easily add new features. We add language model scores as well as contextual features.
For biomedical information extraction, most systems use syntactic patterns on verbs (anchor verbs ) and their arguments. Anchor verbs can be selected by focusing on their arguments. We propose to use predicate-argument structures (PASs), which are outputs of a full parser, to obtain verbs and their arguments. In this paper, we evaluated PAS method by comparing it to a method using part of speech (POSs) pattern matching. POS patterns produced larger results with incorrect arguments, and the results will cause adverse effects on a phase selecting appropriate verbs. ...
In this paper we present a novel, customizable IE paradigm that takes advantage of predicate-argument structures. We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm. It is based on: (1) an extended set of features; and (2) inductive decision tree learning. The experimental results prove our claim that accurate predicate-argument structures enable high quality IE results.
The model used by the CCG parser of Hockenmaier and Steedman (2002b) would fail to capture the correct bilexical dependencies in a language with freer word order, such as Dutch. This paper argues that probabilistic parsers should therefore model the dependencies in the predicate-argument structure, as in the model of Clark et al. (2002), and deﬁnes a generative model for CCG derivations that captures these dependencies, including bounded and unbounded long-range dependencies.
This book is written for all those interested in arguments and arguing—and especially
for students enrolled in courses designed to improve their critical thinking abilities. My
goal in this work is to present enough theory to explain why certain kinds of argument
are good or bad and enough illustrations and examples to show how that theory can
The book includes lively illustrations from contemporary debates and issues and
ample student exercises. Responses to some exercises are provided within the book,
while the remainder are answered in a manual available to instructors.
In predicate-argument structure analysis, it is important to capture non-local dependencies among arguments and interdependencies between the sense of a predicate and the semantic roles of its arguments. However, no existing approach explicitly handles both non-local dependencies and semantic dependencies between predicates and arguments.
This paper presents a comparative study of target dependency structures yielded by several state-of-the-art linguistic parsers. Our approach is to measure the impact of these nonisomorphic dependency structures to be used for string-to-dependency translation. Besides using traditional dependency parsers, we also use the dependency structures transformed from PCFG trees and predicate-argument structures (PASs) which are generated by an HPSG parser and a CCG parser.
This paper examines the use of clue words in argument dialogues. These are special words and phrases directly indicating the structure of the argument to the hearer. Two main conclusions are drawn: I) clue words can occur in conjunction with coherent transmissions, to reduce processing of the hearer 2) clue words must occur with more complex forms of transmission, to facilitate recognition of the argument structure. Interpretation rules to process clues are proposed.
The core-adjunct argument distinction is a basic one in the theory of argument structure. The task of distinguishing between the two has strong relations to various basic NLP tasks such as syntactic parsing, semantic role labeling and subcategorization acquisition. This paper presents a novel unsupervised algorithm for the task that uses no supervised models, utilizing instead state-of-the-art syntactic induction algorithms. This is the ﬁrst work to tackle this task in a fully unsupervised scenario. ...
In Semantic Role Labeling (SRL), it is reasonable to globally assign semantic roles due to strong dependencies among arguments. Some relations between arguments signiﬁcantly characterize the structural information of argument structure. In this paper, we concentrate on thematic hierarchy that is a rank relation restricting syntactic realization of arguments. A loglinear model is proposed to accurately identify thematic rank between two arguments.
This paper describes an algorithm for propagating verb arguments along lexical chains consisting of WordNet relations. The algorithm creates verb argument structures using VerbNet syntactic patterns. In order to increase the coverage, a larger set of verb senses were automatically associated with the existing patterns from VerbNet. The algorithm is used in an in-house Question Answering system for re-ranking the set of candidate answers. Tests on factoid questions from TREC 2004 indicate that the algorithm improved the system performance by 2.4%. ...
These are special words and phrases directly indicating the structure of the argument to the hearer. Two main conclusions are drawn: I) clue words can occur in conjunction with coherent transmissions, to reduce processing of the hearer 2) clue words must occur with more complex forms of transmission, to facilitate recognition of the argument structure. Interpretation rules to process clues are proposed. In addition, a relationship between use of clues and complexity of processing is suggested for the case of exceptional transmission strategies. ...
A method of determining the similarity of nouns on the basis of a metric derived from the distribution of subject, verb and object in a large text corpus is described. The resulting quasi-semantic classification of nouns demonstrates the plausibility of the distributional hypothesis, and has potential application to a variety of tasks, including automatic indexing, resolving nominal compounds, and determining the scope of modification. 1. I N T R O D U C T I O N A variety of linguistic relations apply to sets of semantically similar words. ...
Argumentation has been traditionally the domain of rhetorics and logics, rather than linguistics. Since Aristotle’s time, scholars have studied how ideas are organized in different ways to make an argument. Aristotle was the first person who realized two main constituent of an argument, a Position, and its Justification. Later on Ad Herennium (862BC) expanded the argumentation structure to include five parts: a proposition, a reason, a proof of the reason, an embellishment and a resume.
This work develops and defends a structural view of the nature of mathematics,
which is used to explain a number of striking features of mathematics
that have puzzled philosophers for centuries. It rejects the most widely
held philosophical view of mathematics (Platonism), according to which
mathematics is a science dealing with mathematical objects such as sets and
numbers—objects which are believed not to exist in the physical world.
This paper demonstrates that generating arguments in natural language requires planning at an abstract level, and that the appropriate abstraction cannot be captured by approaches based solely upon coherence relations. An abstraction based planning system is presented which employs operators motivated by empirical study and rhetorical maxims. These operators include a subset of traditional deductive rules of inference, argumentation theoretic rules of refutation, and inductive reasoning patterns. ...
This tutorial aims to provide attendees with a clear notion of how discourse structure is relevant for language technology (LT), what is needed for exploiting discourse structure, what methods and resources are available to support its use, and what more could be done in the future. (a) Discourse chunking and parsing (b) Recognizing arguments and sense of discourse connectives (c) Recognizing and generating entitybased discourse structure (d) Dialogue parsing 3.