tailieunhanh - Báo cáo khoa học: "Unsupervised Learning of Semantic Relation Composition"

This paper presents an unsupervised method for deriving inference axioms by composing semantic relations. The method is independent of any particular relation inventory. It relies on describing semantic relations using primitives and manipulating these primitives according to an algebra. The method was tested using a set of eight semantic relations yielding 78 inference axioms which were evaluated over PropBank. | Unsupervised Learning of Semantic Relation Composition Eduardo Blanco and Dan Moldovan Human Language Technology Research Institute The University of Texas at Dallas Richardson TX 75080 USA eduardo moldovan @ Abstract This paper presents an unsupervised method for deriving inference axioms by composing semantic relations. The method is independent of any particular relation inventory. It relies on describing semantic relations using primitives and manipulating these primitives according to an algebra. The method was tested using a set of eight semantic relations yielding 78 inference axioms which were evaluated over PropBank. 1 Introduction Capturing the meaning of text is a long term goal within the NLP community. Whereas during the last decade the field has seen syntactic parsers mature and achieve high performance the progress in semantics has been more modest. Previous research has mostly focused on relations between particular kind of arguments . semantic roles noun compounds. Notwithstanding their significance they target a fairly narrow text semantics compared to the broad semantics encoded in text. Consider the sentence in Figure 1. Semantic role labelers exclusively detect the relations indicated with solid arrows which correspond to the sentence syntactic dependencies. On top of those roles there are at least three more relations discontinuous arrows that encode semantics other than the verbargument relations. In this paper we venture beyond semantic relation extraction from text and investigate techniques to compose them. We explore the idea of inferring Figure 1 Semantic representation of A man from the Bush administration came before the House Agricultural Committee yesterday to talk about. wsj-0134 0 . a new relation linking the ends of a chain of relations. This scheme informally used previously for combining HYPERNYM with other relations has not been studied for arbitrary pairs of relations. For example it seems adequate to state

• Báo cáo khoa học
• Unsupervised Learning
• Semantic Relation Composition
• Eduardo Blanco and Dan Moldovan
• báo cáo khoa học
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Learning Condensed Feature Representations
• Large Unsupervised Data Sets
• Supervised Learning
• Unsupervised co learning
• downward entailing operators
• Cristian Danescu
• Narrative Schemas and their Participants
• Nathanael Chambers and Dan Jurafsky
• xử lý ngôn ngữ tự nhiên
• Unsupervised Multilingual Learning
• Morphological Segmentation
• Benjamin Snyder and Regina Barzilay
• Narrative Event Chains
• Analyzing the Errors
• Percy Liang Dan Klein
• Unsupervised Learning of Acoustic
• Sub word Units
• Balakrishnan Varadarajan
• Supervised
• Sentence Compression
• mô hình ngôn ngữ
• Field Segmentation Models
• Information Extraction
• Annealing Techniques
• Unsupervised Statistical Language Learning
• Noah A
• Smith and Jason Eisner
• Unsupervised Learning of Arabic Stemming
• a Parallel Corpus
• Monica Rogati Scott McCarley Yiming Yang
• Dependency Structure
• Language Modeling
• Unsupervised Learning – Clustering
• Partitional clustering
• Hierarchical clustering
• Expectation Maximization (EM) algorithm
• Incremental Clustering
• k Means algorithm
• Multi criteria decision making
• Combinative distance based
• Indian banks
• Hybrid unsupervised learning
• Multi criteria decision making approach
• Public sector banks
• Combinative Distance based Assessment
• K Means Clustering Performance
• BMC Bioinformatics
• Cell detection
• Deep learning
• Semi supervised learning
• Unsupervised domain adaptation
• Generalization capabilities
• Animal behavior
• Shape matching
• Shape context
• Active shape model
• Social sciences and humanities
• Self reported experiences
• Corruption on twitter
• Unsupervised machine learning
• Natural language processing
• Lecture Introduction to Machine learning and Data mining
• Bài giảng Học máy và khai phá dữ liệu
• Machine learning and data mining
• Partition based clustering
• Glioblastoma multiforme
• Deep belief network
• Gene expression
• Model selection
• Machine learning
• Unsupervised clustering
• Hyperparameter optimization
• Scikit learn
• Recommendation system
• Personalized web search
• Reinforcement learning
• Cooperative learning
• Probability
• random variables
• Joint probability
• discrete distributions
• Environmental advances
• Environmental contaminant mixtures
• Thyroid hormones
• Machine learning techniques
• Principal component analysis