TAILIEUCHUNG - Báo cáo khoa học: "An Error Analysis of Relation Extraction in Social Media Documents"

The annotated mentions in the Corpus are single or multi-word expressions which refer to a particular real world or abstract entity. The mentions are annotated to indicate sets of mentions which constitute co-reference groups referring to the same entity. Five relationships are annotated between these entities: PartOf, FeatureOf, Produces, InstanceOf, and MemberOf. One significant difference between these relation annotations and those in the ACE Corpus is that the former are relations between sets of mentions (the co-reference groups) rather than between individual mentions | An Error Analysis of Relation Extraction in Social Media Documents Gregory Ichneumon Brown University of Colorado at Boulder Boulder Colorado browngp@ Abstract Relation extraction in documents allows the detection of how entities being discussed in a document are related to one another . part-of . This paper presents an analysis of a relation extraction system based on prior work but applied to the . Power and Associates Sentiment Corpus to examine how the system works on documents from a range of social media. The results are examined on three different subsets of the JDPA Corpus showing that the system performs much worse on documents from certain sources. The proposed explanation is that the features used are more appropriate to text with strong editorial standards than the informal writing style of blogs. 1 Introduction To summarize accurately determine the sentiment or answer questions about a document it is often necessary to be able to determine the relationships between entities being discussed in the document such as part-of or member-of . In the simple sentiment example Example I bought a new car yesterday. I love the powerful engine. determining the sentiment the author is expressing about the car requires knowing that the engine is a part of the car so that the positive sentiment being expressed about the engine can also be attributed to the car. In this paper we examine our preliminary results from applying a relation extraction system to the 64 . Power and Associates JDPA Sentiment Corpus Kessler et al. 2010 . Our system uses lexical features from prior work to classify relations and we examine how the system works on different subsets from the JDPA Sentiment Corpus breaking the source documents down into professionally written reviews blog reviews and social networking reviews. These three document types represent quite different writing styles and we see significant difference in how the relation extraction system performs .

• Báo cáo khoa học
• An Error Analysis
• Relation Extraction
• Social Media Documents
• báo cáo khoa học
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Data reduction
• Error analysis
• Probability distributions
• Monte carlo techniques
• Least squares fit
• Arbitrary function
• Fitting composite curves
• Direct application
• Comparative analysis of LDPC
• BCH codes error correcting capabilities
• Regular LDPC
• Codes are examined
• Error correcting abilities
• Nuclear engineering and technology
• A classification of electrical component failures
• Human error types in South Korean NPPs
• Human error types
• Root causes in K HPES
• Human errorRelated event root cause Analysis Method Plus
• BMC Bioinformatics
• Cell image segmentation
• Cell assays
• Performance measure
• Misclassification error rate
• Total error rate
• Standard error
• Confidence interval
• C
• Huang University of Houston
• Software Error Detection
• Testing and Analysis
• test case selection
• Software
• A Graphical Interface
• MT Evaluation and Error Analysis
• Meritxell Gonz
• A Tool for Error Analysis
• Machine Translation Output
• Sara Stymne
• Using Generation for Grammar Analysis
• Error Detection
• Michael Wayne
• xử lý ngôn ngữ tự nhiên
• Machine Aided Error Correction Environment
• Korean Morphological Analysis
• Part of Speech Tagging
• mô hình ngôn ngữ
• Vietnam Journal of Mechanics
• The error estimate
• The convergence rate for h
• p refinement
• The finite element analysis
• Three dimensional elastostatic mechanics problems
• A novel qEEG measure of teamwork for human error analysis
• An EEG hyperscanning study
• The collaborative process through electroencephalogram
• The neural synchronization in EEGs
• Protein–protein interaction
• Kernel methods
• Kernel similarity
• Intermediate accounting
• Lecture Intermediate accounting
• Financial reporting
• Accounting changes
• Index modulation
• IM OFDM
• Coordinate interleaving
• Pair wise error probability
• Symbol error probability
• Maximum likelihood detection
• Nakagami m fading
• Duplex sequence
• Low frequency variants
• Error correction
• Original DNA molecules
• Frequency response function
• Iteration method
• Mean square
• Stochastic linearization for nonlinear oscillators
• Global local mean square error criterion
• Error analysis on English compositions
• English compositions
• Paragraphs of Vietnamese students
• Vietnamese students
• Theoretical framework
• Round off errors
• Symbolic execution
• Fixed point
• Floating point
• Modern error analysis
• Target enrichment
• Long range PCR
• Sequence error
• Divide and conquer
• PacBio amplicon analysis
• Genome Biology
• Deep sequencing
• Error rate
• Hotspot mutation
• Next generation sequencing
• PCR enrichment
• Numerical analysis
• Mathematical preliminaries
• Solutions of equations
• Polynomial approximation
• Numerical differentiation
• Ordinary differential equations
• Noun clauses
• Use of noun clauses made
• An analysis of errors
• Van Hien University
• Need analysis of interactive karaoke multimedia
• Childhood’s verbal communication ability
• Causes of errors
• English education department