Tools: Team Three - Revision history

ColinGreenstreet: /* Useful Links */

2016-07-06T06:56:10Z

‎Useful Links

ColinGreenstreet at 12:06, June 26, 2016

2016-06-26T12:06:59Z

ColinGreenstreet at 12:06, June 26, 2016

2016-06-26T12:06:18Z

ColinGreenstreet at 11:54, June 26, 2016

2016-06-26T11:54:14Z

ColinGreenstreet at 11:51, June 26, 2016

2016-06-26T11:51:59Z

ColinGreenstreet: /* Useful Links */

2016-06-26T11:19:03Z

‎Useful Links

ColinGreenstreet: /* Useful Links */

2016-06-26T11:15:44Z

‎Useful Links

ColinGreenstreet: /* Useful Links */

2016-06-26T11:11:57Z

‎Useful Links

ColinGreenstreet: /* Useful Links */

2016-06-26T11:09:53Z

‎Useful Links

ColinGreenstreet at 11:07, June 26, 2016

2016-06-26T11:07:23Z

← Older revision		Revision as of 06:56, July 6, 2016
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	[http://marinelives-theshippingnews.org/blog/2014/05/22/how-long-did-it-take/ Colin Greenstreet, 'How long did it take?', The Shipping News blog article, Mat 22, 2014]		[http://marinelives-theshippingnews.org/blog/2014/05/22/how-long-did-it-take/ Colin Greenstreet, 'How long did it take?', The Shipping News blog article, Mat 22, 2014]
		+
		+	[http://abbymullen.org/named-entity-extraction-productive-failure/ Abby Mullen, Named Entity Extraction: Productive Failure? April 18th, 2015. Blog article]
		+	- Discusses automatic extraction of place names through named entity recognition, using the software package "r"
		+	- Extracts place names from the 6-volume document collection 'Naval Documents Related to the United States Wars with the Barbary Powers'
		+	- Identifies problem of confusion between place names and US naval ships named after places, e.g. the USS Chesapeake

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@@ Line 17: / Line 17: @@
 ===Stanford Named Entity Tagger===
-For an assessment of the accuracy of the Standford Named Entity Tagger and Standford Named Entity Recogniser using English High Court of Admiralty data see [https://www.academia.edu/6551336/Dominique_Ritze_Caecilia_Zirn_Colin_Greenstreet_Kai_Eckert_Simone_Paolo_Ponzetto_Named_Entities_in_Court_The_Marine_Lives_Corpus_May_2014_ Dominique Ritze et al., Named Entities in Court: The MarineLives Corpus (May, 2014)]
+[[File:Stanford NETagger HCA1368f35r 26062016.PNG|600px|thumb|left|Stanford Named Entity Tagger - Extract from [[HCA 13/68 f.35r Annotate|HCA 13/68 f.35r]]]]
-[[File:Stanford NETagger HCA1368f35r 26062016.PNG|600px|thumb|right|Stanford Named Entity Tagger - Extract from [[HCA 13/68 f.35r Annotate|HCA 13/68 f.35r]]]]
+For an assessment of the accuracy of the Standford Named Entity Tagger and Standford Named Entity Recogniser using English High Court of Admiralty data see [https://www.academia.edu/6551336/Dominique_Ritze_Caecilia_Zirn_Colin_Greenstreet_Kai_Eckert_Simone_Paolo_Ponzetto_Named_Entities_in_Court_The_Marine_Lives_Corpus_May_2014_ Dominique Ritze et al., Named Entities in Court: The MarineLives Corpus (May, 2014)]
 ----

@@ Line 16: / Line 16: @@
 ===Stanford Named Entity Tagger===
 For an assessment of the accuracy of the Standford Named Entity Tagger and Standford Named Entity Recogniser using English High Court of Admiralty data see [https://www.academia.edu/6551336/Dominique_Ritze_Caecilia_Zirn_Colin_Greenstreet_Kai_Eckert_Simone_Paolo_Ponzetto_Named_Entities_in_Court_The_Marine_Lives_Corpus_May_2014_ Dominique Ritze et al., Named Entities in Court: The MarineLives Corpus (May, 2014)]
 ----

@@ Line 13: / Line 13: @@
 Team members will have an opportunity to work with, and improve upon, a MarineLives dataset for C17th ship sailing times between ports and dwell time in ports
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 [ADD DATA
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 ==Useful Links==

@@ Line 32: / Line 32: @@
 ----
 ==Names Entity Recognisers==
-"Stanford NER is a Java implementation of a Named Entity Recognizer. Named Entity Recognition (NER) labels sequences of words in a text which are the names of things, such as person and company names, or gene and protein names. It comes with well-engineered feature extractors for Named Entity Recognition, and many options for defining feature extractors. Included with the download are good named entity recognizers for English, particularly for the 3 classes (PERSON, ORGANIZATION, LOCATION).
+[ADD DATA
 ----
 ===Stanford Named Entity Tagger===
 [[File:Stanford NETagger HCA1368f35r 26062016.PNG|600px|thumb|left|Stanford Named Entity Tagger - Extract from [[HCA 13/68 f.35r Annotate|HCA 13/68 f.35r]]]]
 ----
@@ Line 73: / Line 67: @@
 [http://nlp.stanford.edu/software/CRF-NER.shtml Stanford Natural Language Processing Group: Software > Stanford Named Entity Recognizer (NER)]
 [http://nlp.stanford.edu:8080/ner/ Online Stanford Named Entity Tagger]

@@ Line 57: / Line 57: @@
 [http://marinelives-theshippingnews.org/blog/2014/05/22/how-long-did-it-take/ Colin Greenstreet, 'How long did it take?', The Shipping News blog article, Mat 22, 2014]
-[http://nlp.stanford.edu/software/CRF-NER.shtml Stanford Natural Language Processing Group: Software > Stanford Named Entity Recognizer (NER)]
+----
 [http://www-nlp.stanford.edu/software/jenny-ner-2007.ppt Jenny Rose Finkel, Stanford University, March 9, 2007]
-[http://nlp.stanford.edu:8080/ner/ Online Stanford Named Entity Tagger]
+[https://sergey-tihon.github.io/Stanford.NLP.NET/StanfordCoreNLP.html Stanford CoreNLP for .NET]
 [http://nlp.stanford.edu:8080/parser/ Stanford Parser for ,NET (A statistical parser)]
@@ Line 67: / Line 72: @@
 - A natural language parser is a program that works out the grammatical structure of sentences, for instance, which groups of words go together (as "phrases") and which words are the subject or object of a verb. Probabilistic parsers use knowledge of language gained from hand-parsed sentences to try to produce the most likely analysis of new sentences. These statistical parsers still make some mistakes, but commonly work rather well. Their development was one of the biggest breakthroughs in natural language processing in the 1990s. You can try out the Stanford parser online.
-[https://sergey-tihon.github.io/Stanford.NLP.NET/StanfordSUTime.html Stanford Temoporal Tagger: SUTime for .NET]
+[http://nlp.stanford.edu/software/CRF-NER.shtml Stanford Natural Language Processing Group: Software > Stanford Named Entity Recognizer (NER)]
 - SUTime is a library for recognizing and normalizing time expressions. SUTime is available as part of the Stanford CoreNLP pipeline and can be used to annotate documents with temporal information. It is a deterministic rule-based system designed for extensibility.

← Older revision		Revision as of 11:11, June 26, 2016
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	[http://nlp.stanford.edu:8080/ner/ Online Stanford Named Entity Tagger]		[http://nlp.stanford.edu:8080/ner/ Online Stanford Named Entity Tagger]
		+
		+	[http://nlp.stanford.edu:8080/parser/ Stanford Parser for ,NET (A statistical parser)
		+
		+	- A natural language parser is a program that works out the grammatical structure of sentences, for instance, which groups of words go together (as "phrases") and which words are the subject or object of a verb. Probabilistic parsers use knowledge of language gained from hand-parsed sentences to try to produce the most likely analysis of new sentences. These statistical parsers still make some mistakes, but commonly work rather well. Their development was one of the biggest breakthroughs in natural language processing in the 1990s. You can try out the Stanford parser online.

← Older revision		Revision as of 11:07, June 26, 2016
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	===Stanford Named Entity Tagger===		===Stanford Named Entity Tagger===

−	[[File:Stanford NETagger HCA1368f35r 26062016.PNG\|600px\|left\|Stanford Named Entity Tagger - Extract from HCA 13/68 f.35r Annotate]]	+	[[File:Stanford NETagger HCA1368f35r 26062016.PNG\|600px\|thumb\|left\|Stanford Named Entity Tagger - Extract from [[HCA 13/68 f.35r Annotate\|HCA 13/68 f.35r]]]]