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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2017

23.01.2017

Co-training for Implicit Discourse Relation Recognition Based on Manual and Distributed Features

verfasst von: Changxing Wu, Xiaodong Shi, Jinsong Su, Yidong Chen, Yanzhou Huang

Erschienen in: Neural Processing Letters | Ausgabe 1/2017

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Abstract

Implicit discourse relation recognition aims to discover the semantic relation between two sentences where the discourse connective is absent. Due to the lack of labeled data, previous work tries to generate additional training data automatically by removing discourse connectives from explicit discourse relation instances. However, using these artificial data indiscriminately has been proven to degrade the performance of implicit discourse relation recognition. To address this problem, we propose a co-training approach based on manual features and distributed features, which identifies useful instances from these artificial data to enlarge the labeled data. In addition, the distributed features are learned via recursive autoencoder based approaches, capable of capturing to some extent the semantics of sentences which is valuable for implicit discourse relation recognition. Experiment results on both the PDTB and CDTB data sets indicate that: (1) The learned distributed features are complementary to the manual features, and thus suitable for co-training. (2) Our proposed co-training approach can use these artificial data effectively, and significantly outperforms the baselines.
Vorheriger Artikel Multiple Instance Learning via Semi-supervised Laplacian TSVM
Nächster Artikel A Generalized Logistic Link Function for Cumulative Link Models in Ordinal Regression

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Fußnoten
1
In the PDTB, discourse relations are mainly defined between two clauses or sentences. Here, we use sentences for simplicity.
 
2
For example, nonetheless is mapped into the Comparison relation.
 
3
In this paper, we model implicit discourse relation recognition as four binary classification tasks (see Sect. 4.1).
 
4
A number of classifiers can be used, including Maximum Entropy (ME) and so on. In our experiments, SVM achieves the best results.
 
5
Randomly duplicate positive instances with replacement until the number of positive and negative instances are equal.
 
6
The FBIS is a bilingual sentence aligned corpus, which consists of 237,870 Chinese-English sentence pairs with 6.72M Chinese words and 8.85M English words.
 
7
We can also get artificial implicit instances from arbitrary text following the method in [5]. However, these artificial instances are much more noisy because it is hard to identify the positions of their arguments.
 
8
We use all the selected artificial instances until the iteration \(K=200\) in Algorithm 1.
 
9
The pdtb-parse toolkit also marks EntRel (entity-based coherence) instances as implicit discourse relation.
 
11
In Chinese, explicit instances account for about 18.0%.
 
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Metadaten
Titel
Co-training for Implicit Discourse Relation Recognition Based on Manual and Distributed Features
verfasst von
Changxing Wu
Xiaodong Shi
Jinsong Su
Yidong Chen
Yanzhou Huang
Publikationsdatum
23.01.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2017
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9582-x

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