8,079 research outputs found
Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis
This paper fills a gap in aspect-based sentiment analysis and aims to present
a new method for preparing and analysing texts concerning opinion and
generating user-friendly descriptive reports in natural language. We present a
comprehensive set of techniques derived from Rhetorical Structure Theory and
sentiment analysis to extract aspects from textual opinions and then build an
abstractive summary of a set of opinions. Moreover, we propose aspect-aspect
graphs to evaluate the importance of aspects and to filter out unimportant ones
from the summary. Additionally, the paper presents a prototype solution of data
flow with interesting and valuable results. The proposed method's results
proved the high accuracy of aspect detection when applied to the gold standard
dataset
Better Document-level Sentiment Analysis from RST Discourse Parsing
Discourse structure is the hidden link between surface features and
document-level properties, such as sentiment polarity. We show that the
discourse analyses produced by Rhetorical Structure Theory (RST) parsers can
improve document-level sentiment analysis, via composition of local information
up the discourse tree. First, we show that reweighting discourse units
according to their position in a dependency representation of the rhetorical
structure can yield substantial improvements on lexicon-based sentiment
analysis. Next, we present a recursive neural network over the RST structure,
which offers significant improvements over classification-based methods.Comment: Published at Empirical Methods in Natural Language Processing (EMNLP
2015
Cross-lingual RST Discourse Parsing
Discourse parsing is an integral part of understanding information flow and
argumentative structure in documents. Most previous research has focused on
inducing and evaluating models from the English RST Discourse Treebank.
However, discourse treebanks for other languages exist, including Spanish,
German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same
underlying linguistic theory, but differ slightly in the way documents are
annotated. In this paper, we present (a) a new discourse parser which is
simpler, yet competitive (significantly better on 2/3 metrics) to state of the
art for English, (b) a harmonization of discourse treebanks across languages,
enabling us to present (c) what to the best of our knowledge are the first
experiments on cross-lingual discourse parsing.Comment: To be published in EACL 2017, 13 page
Hypotheses, evidence and relationships: The HypER approach for representing scientific knowledge claims
Biological knowledge is increasingly represented as a collection of (entity-relationship-entity) triplets. These are queried, mined, appended to papers, and published. However, this representation ignores the argumentation contained within a paper and the relationships between hypotheses, claims and evidence put forth in the article. In this paper, we propose an alternate view of the research article as a network of 'hypotheses and evidence'. Our knowledge representation focuses on scientific discourse as a rhetorical activity, which leads to a different direction in the development of tools and processes for modeling this discourse. We propose to extract knowledge from the article to allow the construction of a system where a specific scientific claim is connected, through trails of meaningful relationships, to experimental evidence. We discuss some current efforts and future plans in this area
Learning Recursive Segments for Discourse Parsing
Automatically detecting discourse segments is an important preliminary step
towards full discourse parsing. Previous research on discourse segmentation
have relied on the assumption that elementary discourse units (EDUs) in a
document always form a linear sequence (i.e., they can never be nested).
Unfortunately, this assumption turns out to be too strong, for some theories of
discourse like SDRT allows for nested discourse units. In this paper, we
present a simple approach to discourse segmentation that is able to produce
nested EDUs. Our approach builds on standard multi-class classification
techniques combined with a simple repairing heuristic that enforces global
coherence. Our system was developed and evaluated on the first round of
annotations provided by the French Annodis project (an ongoing effort to create
a discourse bank for French). Cross-validated on only 47 documents (1,445
EDUs), our system achieves encouraging performance results with an F-score of
73% for finding EDUs.Comment: published at LREC 201
Cross-lingual and cross-domain discourse segmentation of entire documents
Discourse segmentation is a crucial step in building end-to-end discourse
parsers. However, discourse segmenters only exist for a few languages and
domains. Typically they only detect intra-sentential segment boundaries,
assuming gold standard sentence and token segmentation, and relying on
high-quality syntactic parses and rich heuristics that are not generally
available across languages and domains. In this paper, we propose statistical
discourse segmenters for five languages and three domains that do not rely on
gold pre-annotations. We also consider the problem of learning discourse
segmenters when no labeled data is available for a language. Our fully
supervised system obtains 89.5% F1 for English newswire, with slight drops in
performance on other domains, and we report supervised and unsupervised
(cross-lingual) results for five languages in total.Comment: To appear in Proceedings of ACL 201
Fast Rhetorical Structure Theory Discourse Parsing
In recent years, There has been a variety of research on discourse parsing,
particularly RST discourse parsing. Most of the recent work on RST parsing has
focused on implementing new types of features or learning algorithms in order
to improve accuracy, with relatively little focus on efficiency, robustness, or
practical use. Also, most implementations are not widely available. Here, we
describe an RST segmentation and parsing system that adapts models and feature
sets from various previous work, as described below. Its accuracy is near
state-of-the-art, and it was developed to be fast, robust, and practical. For
example, it can process short documents such as news articles or essays in less
than a second
A PDTB-Styled End-to-End Discourse Parser
We have developed a full discourse parser in the Penn Discourse Treebank
(PDTB) style. Our trained parser first identifies all discourse and
non-discourse relations, locates and labels their arguments, and then
classifies their relation types. When appropriate, the attribution spans to
these relations are also determined. We present a comprehensive evaluation from
both component-wise and error-cascading perspectives.Comment: 15 pages, 5 figures, 7 table
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