2,722 research outputs found
Argumentation Mining in User-Generated Web Discourse
The goal of argumentation mining, an evolving research field in computational
linguistics, is to design methods capable of analyzing people's argumentation.
In this article, we go beyond the state of the art in several ways. (i) We deal
with actual Web data and take up the challenges given by the variety of
registers, multiple domains, and unrestricted noisy user-generated Web
discourse. (ii) We bridge the gap between normative argumentation theories and
argumentation phenomena encountered in actual data by adapting an argumentation
model tested in an extensive annotation study. (iii) We create a new gold
standard corpus (90k tokens in 340 documents) and experiment with several
machine learning methods to identify argument components. We offer the data,
source codes, and annotation guidelines to the community under free licenses.
Our findings show that argumentation mining in user-generated Web discourse is
a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in
User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
Reasoning is a crucial part of natural language argumentation. To comprehend
an argument, one must analyze its warrant, which explains why its claim follows
from its premises. As arguments are highly contextualized, warrants are usually
presupposed and left implicit. Thus, the comprehension does not only require
language understanding and logic skills, but also depends on common sense. In
this paper we develop a methodology for reconstructing warrants systematically.
We operationalize it in a scalable crowdsourcing process, resulting in a freely
licensed dataset with warrants for 2k authentic arguments from news comments.
On this basis, we present a new challenging task, the argument reasoning
comprehension task. Given an argument with a claim and a premise, the goal is
to choose the correct implicit warrant from two options. Both warrants are
plausible and lexically close, but lead to contradicting claims. A solution to
this task will define a substantial step towards automatic warrant
reconstruction. However, experiments with several neural attention and language
models reveal that current approaches do not suffice.Comment: Accepted as NAACL 2018 Long Paper; see details on the front pag
Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters
Natural Language Inference (NLI) has been extensively studied by the NLP
community as a framework for estimating the semantic relation between sentence
pairs. While early work identified certain biases in NLI models, recent
advancements in modeling and datasets demonstrated promising performance. In
this work, we further explore the direct zero-shot applicability of NLI models
to real applications, beyond the sentence-pair setting they were trained on.
First, we analyze the robustness of these models to longer and out-of-domain
inputs. Then, we develop new aggregation methods to allow operating over full
documents, reaching state-of-the-art performance on the ContractNLI dataset.
Interestingly, we find NLI scores to provide strong retrieval signals, leading
to more relevant evidence extractions compared to common similarity-based
methods. Finally, we go further and investigate whole document clusters to
identify both discrepancies and consensus among sources. In a test case, we
find real inconsistencies between Wikipedia pages in different languages about
the same topic.Comment: Findings of EMNLP 202
Proceedings of the Conference on Natural Language Processing 2010
This book contains state-of-the-art contributions to the 10th
conference on Natural Language Processing, KONVENS 2010
(Konferenz zur Verarbeitung natürlicher Sprache), with a focus
on semantic processing.
The KONVENS in general aims at offering a broad perspective
on current research and developments within the interdisciplinary
field of natural language processing. The central theme
draws specific attention towards addressing linguistic aspects
ofmeaning, covering deep as well as shallow approaches to semantic
processing. The contributions address both knowledgebased
and data-driven methods for modelling and acquiring
semantic information, and discuss the role of semantic information
in applications of language technology.
The articles demonstrate the importance of semantic processing,
and present novel and creative approaches to natural
language processing in general. Some contributions put their
focus on developing and improving NLP systems for tasks like
Named Entity Recognition or Word Sense Disambiguation, or
focus on semantic knowledge acquisition and exploitation with
respect to collaboratively built ressources, or harvesting semantic
information in virtual games. Others are set within the
context of real-world applications, such as Authoring Aids, Text
Summarisation and Information Retrieval. The collection highlights
the importance of semantic processing for different areas
and applications in Natural Language Processing, and provides
the reader with an overview of current research in this field
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