640,450 research outputs found
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
Discourse structure and information structure : interfaces and prosodic realization
In this paper we review the current state of research on the issue of discourse structure (DS) / information structure (IS) interface. This field has received a lot of attention from discourse semanticists and pragmatists, and has made substantial progress in recent years. In this paper we summarize the relevant studies. In addition, we look at the issue of DS/ISinteraction at a different level—that of phonetics. It is known that both information structure and discourse structure can be realized prosodically, but the issue of phonetic interaction between the prosodic devices they employ has hardly ever been discussed in this context. We think that a proper consideration of this aspect of DS/IS-interaction would enrich our understanding of the phenomenon, and hence we formulate some related research-programmatic positions
A discourse analysis of the Tai Dam chronicle
This paper is a discourse study of the Tai Dam chronicle Kwam To Muang. It focuses on rhetorical structure and information structure. The former includes rhyming structure, parallel structure, cyclical structure, and listing structure, which function to facilitate memorization and also provide a linkage to the text. The information structure is analyzed in terms of the bipartite discourse structure, i.e. storyline elements and Nonstoryline elements. The storyline material is signalled by the preverbal auxiliary caŋ² ‘consequently, then’ and verb types, namely, event proper, motion, and action verbs. Nonstoryline elements include supportive materials which are off the storyline. They include setting, background, collateral, and cohesion. The setting is characterized by descriptive and stative verbs. Background information is marked by non-punctiliar verbs. Collateral information is expressed by a negation having the negative marker baw³ ‘not’. And cohesion is realized by rhetorical structure and repetitive clauses
A Deep Sequential Model for Discourse Parsing on Multi-Party Dialogues
Discourse structures are beneficial for various NLP tasks such as dialogue
understanding, question answering, sentiment analysis, and so on. This paper
presents a deep sequential model for parsing discourse dependency structures of
multi-party dialogues. The proposed model aims to construct a discourse
dependency tree by predicting dependency relations and constructing the
discourse structure jointly and alternately. It makes a sequential scan of the
Elementary Discourse Units (EDUs) in a dialogue. For each EDU, the model
decides to which previous EDU the current one should link and what the
corresponding relation type is. The predicted link and relation type are then
used to build the discourse structure incrementally with a structured encoder.
During link prediction and relation classification, the model utilizes not only
local information that represents the concerned EDUs, but also global
information that encodes the EDU sequence and the discourse structure that is
already built at the current step. Experiments show that the proposed model
outperforms all the state-of-the-art baselines.Comment: Accepted to AAAI 201
Structuring information through gesture and intonation
Face-to-face communication is multimodal. In unscripted spoken discourse we can observe the interaction of several “semiotic layers”, modalities of information such as syntax, discourse structure, gesture, and intonation. We explore the role of gesture and intonation in structuring and aligning information in spoken discourse through a study of the co-occurrence of pitch accents and gestural apices. Metaphorical spatialization through gesture also plays a role in conveying the contextual relationships between the speaker, the government and other external forces in a naturally-occurring political speech setting
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse
structure for machine translation evaluation. We first design discourse-aware
similarity measures, which use all-subtree kernels to compare discourse parse
trees in accordance with the Rhetorical Structure Theory (RST). Then, we show
that a simple linear combination with these measures can help improve various
existing machine translation evaluation metrics regarding correlation with
human judgments both at the segment- and at the system-level. This suggests
that discourse information is complementary to the information used by many of
the existing evaluation metrics, and thus it could be taken into account when
developing richer evaluation metrics, such as the WMT-14 winning combined
metric DiscoTKparty. We also provide a detailed analysis of the relevance of
various discourse elements and relations from the RST parse trees for machine
translation evaluation. In particular we show that: (i) all aspects of the RST
tree are relevant, (ii) nuclearity is more useful than relation type, and (iii)
the similarity of the translation RST tree to the reference tree is positively
correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse
analysis. Computational Linguistics, 201
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