F1 score - PDFSEARCH.IO - Document Search Engine

F1 score
Results: 49

#	Item
1	Parser Evaluation Evaluation Parser Evaluation Add to Reading List Source URL: cl.indiana.edu Language: English - Date: 2015-11-17 13:42:01 Compiler construction Parsing Statistics Parseval F1 score Parsing algorithms Analysis Computing
2	Parser Evaluation Evaluation PARSEVAL Other metrics Add to Reading List Source URL: cl.indiana.edu Language: English - Date: 2015-11-12 13:45:53 Compiler construction Parsing Statistics Parseval F1 score Parsing algorithms Analysis Computing
3	JMLR: Workshop and Conference Proceedings 25:269–284, 2012 Asian Conference on Machine Learning On Using Nearly-Independent Feature Families for High Precision and Confidence Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2012-12-03 17:28:19 Statistical classification Machine learning Linear classifier Precision and recall Support vector machine Binary classification F1 score Supervised learning Classifier Probability distribution
4	How Effective is Query Expansion for Finding Novel Information? Min Zhang1 , Chuan Lin2 , and Shaoping Ma1 1 2 Add to Reading List Source URL: www.thuir.org Language: English - Date: 2008-03-24 08:33:59 Information retrieval evaluation Computational linguistics Natural language processing Query expansion Relevance feedback Relevance Precision and recall Information retrieval Thesaurus Text Retrieval Conference F1 score Semantic similarity
5	Are All Commas Equal? Detecting Coordination in the Penn Treebank Wolfgang Maier Sandra Kübler Add to Reading List Source URL: cl.indiana.edu Language: English - Date: 2014-07-28 14:53:29 Linguistics Syntax Cognitive science Corpus linguistics Coordination Treebank Conjunct Parsing Punctuation Precision and recall Comma F1 score
6	TREC_EVAL: IR Evaluation Draft: Module name: Evaluation in Information Retrieval 2. Scope: This module addresses the methods used to evaluate an Information Retrieval system. We focus on evaluating a system Add to Reading List Source URL: curric.dlib.vt.edu Language: English - Date: 2011-01-19 20:54:35 Information science Information retrieval Information retrieval evaluation Natural language processing Bioinformatics Precision and recall Relevance Text Retrieval Conference F1 score Document retrieval Discounted cumulative gain Search engine indexing
7	Improving on Recall Errors for Coreference Resolution Claire Cardie Department of Computer Science Cornell University Add to Reading List Source URL: www.akbc.ws Language: English - Date: 2015-04-02 16:06:46 Parts of speech Semantics Coreference Statistical natural language processing Anaphora Precision and recall Recall F1 score Antecedent Linguistics Science Statistics
8	How Good is 85%? A Survey Tool to Connect Classifier Evaluation to Acceptability of Accuracy Matthew Kay Computer Science & Engineering \| dub, University of Washington Add to Reading List Source URL: www.mjskay.com Language: English - Date: 2015-04-25 22:02:34 Statistical classification Natural language processing Biostatistics Accuracy and precision F1 score Recommender system Gain Classifier Precision and recall Statistics Information retrieval Machine learning
9	JMLR: Workshop and Conference Proceedings 34:193–206, 2014 Proceedings of the 12th ICGI Evaluation of selection in context-free grammar learning systems Add to Reading List Source URL: jmlr.org Language: English - Date: 2014-08-26 06:56:50 Linguistics Natural language processing Information retrieval Evaluation of machine translation Precision and recall F1 score Treebank Recall Statistics Science Computational linguistics
10	How Good is 85%? A Survey Tool to Connect Classifier Evaluation to Acceptability of Accuracy Matthew Kay Computer Science & Engineering \| dub, University of Washington Add to Reading List Source URL: dub.washington.edu Language: English - Date: 2015-04-20 16:18:12 Statistical classification Natural language processing Biostatistics Accuracy and precision F1 score Recommender system Gain Classifier Precision and recall Statistics Information retrieval Machine learning