tailieunhanh - Báo cáo khoa học: "Leveraging Structural Relations for Fluent Compressions at Multiple Compression Rates"
Prior approaches to sentence compression have taken low level syntactic constraints into account in order to maintain grammaticality. We propose and successfully evaluate a more comprehensive, generalizable feature set that takes syntactic and structural relationships into account in order to sustain variable compression rates while making compressed sentences more coherent, grammatical and readable. | Leveraging Structural Relations for Fluent Compressions at Multiple Compression Rates Sourish Chaudhuri Naman K. Gupta Noah A. Smith Carolyn P. Rosé Language Technologies Institute Carnegie Mellon University Pittsburgh PA-15213 USA. sourishc nkgupta nasmith cprose @ Abstract Prior approaches to sentence compression have taken low level syntactic constraints into account in order to maintain grammaticality. We propose and successfully evaluate a more comprehensive generalizable feature set that takes syntactic and structural relationships into account in order to sustain variable compression rates while making compressed sentences more coherent grammatical and readable. 1 Introduction We present an evaluation of the effect of syntactic and structural constraints at multiple levels of granularity on the robustness of sentence compression at varying compression rates. Our evaluation demonstrates that the new feature set produces significantly improved compressions across a range of compression rates compared to existing state-of-the-art approaches. Thus we name our system for generating compressions the Adjustable Rate Compressor ARC . Knight and Marcu 2000 K M henceforth presented two approaches to the sentence compression problem one using a noisy channel model the other using a decision-based model. The performances of the two models were comparable though their experiments suggested that the noisy channel model degraded more smoothly than the decision-based model when tested on out-of-domain data. Riezler et al. 2003 applied linguistically rich LFG grammars to a sentence compression system. Turner and Charniak 2005 achieved similar performance to K M using an unsupervised approach that induced rules from the Penn Treebank. A variety of feature encodings have previously been explored for the problem of sentence compression. Clarke and Lapata 2007 included discourse level features in their framework to leverage context for enhancing coherence. McDonald s .
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