What is an Argument?
A strong argument attempts to persuade the reader to accept a point of view. As such, it
consists of a proposition, a declarative statement which is capable of being argued, and a
proof, a reason or ground which is supported by evidence. The evidence, in turn, is composed
of relevant facts, opinions based on facts and careful reasoning. If you are analyzing an
argument, you should look for both of these: a proposition and the evidence supporting the
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.
The task of Semantic Role Labeling (SRL) is often divided into two sub-tasks: verb argument identiﬁcation, and argument classiﬁcation. Current SRL algorithms show lower results on the identiﬁcation sub-task. Moreover, most SRL algorithms are supervised, relying on large amounts of manually created data. In this paper we present an unsupervised algorithm for identifying verb arguments, where the only type of annotation required is POS tagging.
We have developed a system that generates evaluative arguments that are tailored to the user, properly arranged and concise. We have also developed an evaluation framework in which the effectiveness of evaluative arguments can be measured with real users. This paper presents the results of a formal experiment we have performed in our framework to verify the influence of argument conciseness on argument effectiveness In the remainder of the paper, we first describe a computational framework for generating evaluative arguments at different levels of conciseness. ...
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.
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 is the ninth book containing examples from the Theory of Complex Functions. We shall here
treat the important Argument Principle, which e.g. is applied in connection with Criteria of Stability
in Cybernetics. Finally, we shall also consider the Many-valued functions and their pitfalls.
Even if I have tried to be careful about this text, it is impossible to avoid errors, in particular in the
first edition. It is my hope that the reader will show some understanding of my situation.
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.
Argumentation schemes are structures or templates for various kinds of arguments. Given the text of an argument with premises and conclusion identiﬁed, we classify it as an instance of one of ﬁve common schemes, using features speciﬁc to each scheme. We achieve accuracies of 63–91% in one-against-others classiﬁcation and 80–94% in pairwise classiﬁcation (baseline = 50% in both cases).
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.
Abstract-like text summarisation requires a means of producing novel summary sentences. In order to improve the grammaticality of the generated sentence, we model a global (sentence) level syntactic structure. We couch statistical sentence generation as a spanning tree problem in order to search for the best dependency tree spanning a set of chosen words. We also introduce a new search algorithm for this task that models argument satisfaction to improve the linguistic validity of the generated tree. ...
In order to build robust automatic abstracting systems, there is a need for better training resources than are currently available. In this paper, we introduce an annotation scheme for scientific articles which can be used to build such a resource in a consistent way. The seven categories of the scheme are based on rhetorical moves of argumentation. Our experimental results show that the scheme is stable, reproducible and intuitive to use.
This paper deals with the reference choices involved in the generation of argumentative text. Since a natual segmentation of discourse into attentional spaces is needed to carry out this task, this paper first proposes an architecture for natural language generation that combines hierarchical planning and focus-guided navigation, a work in its own right. While hierarchical planning spans out an attentional hierarchy of the discourse produced, local navigation fills details into the primitive discourse spaces.
This paper describes a COMIT program that proves the validity of logical arguments expressed in a restricted form of ordinary English. Some special features include its ability to translate an input argument into logical notation in four progressively refined ways, of which the first pertains to propositional logic and the last three to first-order functional logic; and its ability in many cases to select the "correct" logical translation of an argument, i.e., the translation that yields the simplest proof....
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.
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. ...
A challenging problem in open information extraction and text mining is the learning of the selectional restrictions of semantic relations. We propose a minimally supervised bootstrapping algorithm that uses a single seed and a recursive lexico-syntactic pattern to learn the arguments and the supertypes of a diverse set of semantic relations from the Web. We evaluate the performance of our algorithm on multiple semantic relations expressed using “verb”, “noun”, and “verb prep” lexico-syntactic patterns. ...
Despite its substantial coverage, NomBank does not account for all withinsentence arguments and ignores extrasentential arguments altogether. These arguments, which we call implicit, are important to semantic processing, and their recovery could potentially beneﬁt many NLP applications. We present a study of implicit arguments for a select group of frequent nominal predicates. We show that implicit arguments are pervasive for these predicates, adding 65% to the coverage of NomBank.