کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
557931 874817 2011 20 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Automatic identification of discourse markers in dialogues: An in-depth study of like and well
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Automatic identification of discourse markers in dialogues: An in-depth study of like and well
چکیده انگلیسی

The lexical items like and well can serve as discourse markers (DMs), but can also play numerous other roles, such as verb or adverb. Identifying the occurrences that function as DMs is an important step for language understanding by computers. In this study, automatic classifiers using lexical, prosodic/positional and sociolinguistic features are trained over transcribed dialogues, manually annotated with DM information. The resulting classifiers improve state-of-the-art performance of DM identification, at about 90% recall and 79% precision for like (84.5% accuracy, κ = 0.69), and 99% recall and 98% precision for well (97.5% accuracy, κ = 0.88). Automatic feature analysis shows that lexical collocations are the most reliable indicators, followed by prosodic/positional features, while sociolinguistic features are marginally useful for the identification of DM like and not useful for well. The differentiated processing of each type of DM improves classification accuracy, suggesting that these types should be treated individually.

Research highlights▶ We aim at finding occurrences of ‘like’ and ‘well’ serving as discourse markers. ▶ Classifiers are trained using lexical, prosodic and sociolinguistic features. ▶ Results reach κ = 0.69 for ‘like’ and 0.88 for ‘well’ on dialogue transcripts. ▶ Lexical collocations are the most reliable indicators. ▶ The two types are better processed separately than jointly.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Speech & Language - Volume 25, Issue 3, July 2011, Pages 499–518
نویسندگان
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