Automatic sense prediction for implicit discourse relations in text

By Emily Pitler, Annie Louis, Ani Nenkova. Annual Meeting of the Association for Computational Linguistics, 2009

309 citations recorded by the source corpus.

We present a series of experiments on automatically identifying the sense of implicit discourse relations, i.e. relations that are not marked with a discourse connective such as "but" or "because". We work with a corpus of implicit relations present in newspaper text and report results on a test set that is representative of the naturally occurring distribution of senses. We use several linguistically informed features, including polarity tags, Levin verb classes, length of verb phrases, modality, context, and lexical features. In addition, we revisit past approaches using lexical pairs from unannotated text as features, explain some of their shortcomings and propose modifications. Our best combination of features outperforms the baseline from data intensive approaches by 4% for comparison and 16% for contingency.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/0e356cf1c47981200f037ddc07b573ac0b70c2f8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Recognizing Implicit Discourse Relations in the Penn Discourse Treebank (2009, methodology): “In addition, the data set of (Pitler et al., 2009) includes EntRel and AltLex , which are relations in which an implicit connective cannot be inserted between adjacent sentences, whereas ours excludes EntRel and AltLex .”
Identifying Argumentative Discourse Structures in Persuasive Essays (2014, methodology): “In addition, we adopt the ﬁrst word features proposed by Pitler et al. (2009).”
Parsing Argumentation Structures in Persuasive Essays (2016, background): “Pitler, Louis, and Nenkova (2009) proposed an approach for identifying four implicit types of discourse relations in the PDTB and achieved F1 scores between 0 .”
Implicit Discourse Relation Classification: We Need to Talk about Evaluation (2020, methodology): “AS - MANNER The literature has focused on implicit discourse relations from PDTB 2.0 (Pitler et al., 2009; Lin et al., 2009), on which deep learning has yielded substantial performance gains (Chen et al., 2016; Liu and Li, 2016; Lan et al., 2017; Qin et al., 2017; Bai and ∗ Work done while at IBM…”