TAILIEUCHUNG - Báo cáo khoa học: "A Sequencing Model for Situation Entity Classification"

Situation entities (SEs) are the events, states, generic statements, and embedded facts and propositions introduced to a discourse by clauses of text. We report on the first datadriven models for labeling clauses according to the type of SE they introduce. SE classification is important for discourse mode identification and for tracking the temporal progression of a discourse. We show that (a) linguistically-motivated cooccurrence features and grammatical relation information from deep syntactic analysis improve classification accuracy and (b) using a sequencing model provides improvements over assigning labels based on the utterance alone. . | A Sequencing Model for Situation Entity Classification Alexis Palmer Elias Ponvert Jason Baldridge and Carlota Smith Department of Linguistics University of Texas at Austin alexispalmer ponvert jbaldrid carlotasmith @ Abstract Situation entities SEs are the events states generic statements and embedded facts and propositions introduced to a discourse by clauses of text. We report on the first data-driven models for labeling clauses according to the type of SE they introduce. SE classification is important for discourse mode identification and for tracking the temporal progression of a discourse. We show that a linguistically-motivated cooccurrence features and grammatical relation information from deep syntactic analysis improve classification accuracy and b using a sequencing model provides improvements over assigning labels based on the utterance alone. We report on genre effects which support the analysis of discourse modes having characteristic distributions and sequences of SEs. 1 Introduction Understanding discourse requires identifying the participants in the discourse the situations they participate in and the various relationships between and among both participants and situations. Coreference resolution for example is concerned with understanding the relationships between references to discourse participants. This paper addresses the problem of identifying and classifying references to situations expressed in written English texts. Situation entities SEs are the events states generic statements and embedded facts and propositions which clauses introduce Vendler 1967 896 Verkuyl 1972 Dowty 1979 Smith 1991 Asher 1993 Carlson and Pelletier 1995 . Consider the text passage below which introduces an event-type entity in 1 a report-type entity in 2 and a statetype entity in 3 . 1 Sony Corp. has heavily promoted the Video Walkman since the product s introduction last summer 2 but Bob Gerson video editor of This Week in Consumer Electronics says 3

• Báo cáo khoa học
• A Sequencing Model
• Situation Entity Classification
• Alexis Palmer
• Elias Ponvert
• Jason Baldridge
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• BMC Bioinformatics
• Family based sequencing
• Genotype calling
• Hidden Markov model
• Linkage disequilibrium
• Sequencing technologies
• Next generation sequencing
• Heterogeneous tumor
• Phylogenetic tree
• Sequencing errors
• BMC Genomics
• Long read
• Hybrid genome
• Third generation sequencing technologies
• Metarhizium spp genomes
• HLA genotyping
• Whole genome sequencing
• Whole exome sequencing
• Cancer immunogenomics
• Bayesian hierarchical model
• Markov chain Monte Carlo
• Rat genome
• Animal model validation
• BMC Cancer
• Exome sequencing
• Mouse model
• Wild type
• Malignant mesothelioma
• Human counterpart
• Co expression
• Latent factor model
• RNA sequencing
• Single cell
• Illumina sequencing
• Second best base call
• Model based clustering
• Viral quasispecies
• Nucleotide triplets
• Genome Biology
• Intra tumour heterogeneity
• Clonal evolution
• Joint probabilistic model
• Distance dependent
• Chinese restaurant process
• Single cell sequencing
• Somatic mutation calling
• Sensitivity and specificity
• Bayesian inference
• Model based cutoff finding
• Discrete data
• Co expression networks
• Hydra vulgaris
• Biological information
• Non reference model organism
• Copy number variation
• Variational based model
• Significant tumors
• Virus population dynamics
• Error correction
• Linear mixed model
• Viral coinfection
• Sequence analysis
• Hidden Markov Models
• Non essential
• Antimicrobial treatment
• Gerbil genome
• Meriones unguiculatus
• Model organism
• Laboratory gerbils
• Visual systems
• Data quality assessment
• Genome assembly
• Genome sequencing
• Non model organisms
• Single molecule sequencing
• Long reads
• Optical maps
• DNA modifications
• Emerging model organism
• Insect genomes
• Methyl capture sequencing
• DNA methylation
• Animal model
• Cynomolgus monkey
• African green monkey
• Variant calling
• Variant quality
• Variant confidence
• Secondary confirmation
• Inexpensive pipeline
• Streamlining large scale collection
• Bacterial isolates
• Model organism Salmonella enterica
• Structural variation
• Long read sequencing
• Simulated mode
• Human disease
• Long read sequencing platforms
• Nanopore direct RNA sequencing
• Arabidopsis thaliana
• Robustness across species
• Neural network
• RNA molecule
• High throughput sequencing
• Multiple transcripts