Computational Methods for Corpus Annotation and Analysis

Lu, Xiaofei

doi:10.1007/978-94-017-8645-4

Cited by 78 publications

(24 citation statements)

References 73 publications

(90 reference statements)

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“…For syntactic variation, we applied the D-Level Analyser (Lu, 2009) using the D-Level scale (Lu, 2014). This tool builds off of the Stanford Partof-Speech Tagger (Toutanova and Manning, 2000) and the Collins Parser (Collins, 2003) and produces a scaled analysis.…”

Section: Discussionmentioning

confidence: 99%

Predicting Perceived Age: Both Language Ability and Appearance are Important

Plane¹,

Marvasti²,

Egan³

et al. 2018

Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

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When interacting with robots in a situated spoken dialogue setting, human dialogue partners tend to assign anthropomorphic and social characteristics to those robots. In this paper, we explore the age and educational level that human dialogue partners assign to three different robotic systems, including an un-embodied spoken dialogue system. We found that how a robot speaks is as important to human perceptions as the way the robot looks. Using the data from our experiment, we derived prosodic, emotional, and linguistic features from the participants to train and evaluate a classifier that predicts perceived intelligence, age, and education level.

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Section: Discussionmentioning

confidence: 99%

Predicting Perceived Age: Both Language Ability and Appearance are Important

Plane¹,

Marvasti²,

Egan³

et al. 2018

Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

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“…We used the D-Level Analyser (Lu, 2009) to evaluate syntactic variation and complexity of human references using the revised D-Level Scale (Lu, 2014). Figure 3 show a similar syntactic variation in all three datasets.…”

Section: Syntactic Variation and Discourse Phenomenamentioning

confidence: 96%

The E2E Dataset: New Challenges For End-to-End Generation

Novikova¹,

Dušek²,

Rieser³

2017

Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

232

305

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This paper describes the E2E data, a new dataset for training end-to-end, datadriven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area. The E2E dataset poses new challenges: (1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena; (2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances. We also establish a baseline on this dataset, which illustrates some of the difficulties associated with this data.

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“…The results of the 14 syntactic complexity measures generated by the L2SCA can be easily sent to Excel or SPSS for statistical analyses. A detailed, step-by-step procedure for how to use the analyser is illustrated in Lu (2014).…”

Section: The L2 Syntactic Complexity Analyzermentioning

confidence: 99%

Noun Phrase Complexity in EFL Academic Writing: A Corpus-based Study of Postgraduate Academic Writing

Liu¹,

Li²

2016

The Journal of AsiaTEFL

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Noun phrase (NP) centered structures are distinctive syntactic devices in academic discourse. The commonly employed subordination-based complexity measures cannot adequately capture the development of syntactic complexity of noun phrases expected of advanced student academic writing (Biber, Gray, & Poonpon, 2011). Following the call for more research in this area (e.g. Lu, 2011, p. 57), the current study compared noun phrase complexity in two corpora: one is a corpus of MA dissertations written by Chinese EFL students and the other comprises published research articles in applied linguistics journals. The study examined overall noun phrase complexity using an automatic syntactic complexity analyzer and specifically identified features of one aspect of NP complexity: NP postmodification. The quantitative results were further contextualized in a textual analysis of excerpts from the two corpora for demonstrating the significance of NP complexity to the establishment of discourse coherence. Results of the analyses showed significant underdevelopment of NP postmodification complexity in student writing relative to published texts, meanwhile explicating the circumstances under which the difference is meaningful. Implications of the findings for the teaching of EFL academic writing were also discussed.

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Computational Methods for Corpus Annotation and Analysis

Cited by 78 publications

References 73 publications

Predicting Perceived Age: Both Language Ability and Appearance are Important

Predicting Perceived Age: Both Language Ability and Appearance are Important

The E2E Dataset: New Challenges For End-to-End Generation

Noun Phrase Complexity in EFL Academic Writing: A Corpus-based Study of Postgraduate Academic Writing

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