e429103a7a461c9eab6a70f3e3367af04a877ee20989c215e5e520aa81b2a7c0

.gitattributes

@@ -60,3 +60,12 @@ de-en/claims/pattr.de-en.claims.de filter=lfs diff=lfs merge=lfs -text
 de-en/claims/pattr.de-en.claims.en filter=lfs diff=lfs merge=lfs -text
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 de-en/description/pattr.de-en.description.en filter=lfs diff=lfs merge=lfs -text
+de-en/abstract/pattr.de-en.abstract.de filter=lfs diff=lfs merge=lfs -text
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+de-en/description/pattr.de-en.description.en.meta filter=lfs diff=lfs merge=lfs -text
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+de-en/title/pattr.de-en.title.en filter=lfs diff=lfs merge=lfs -text
+de-en/title/pattr.de-en.title.meta filter=lfs diff=lfs merge=lfs -text

de-en/LICENSE

@@ -0,0 +1,331 @@
+THE WORK (AS DEFINED BELOW) IS PROVIDED UNDER THE TERMS OF THIS
+CREATIVE COMMONS PUBLIC LICENSE ("CCPL" OR "LICENSE"). THE WORK IS
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+1. Definitions
+ a. "Adaptation" means a work based upon the Work, or upon the Work and
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de-en/README

@@ -0,0 +1,129 @@
+P A T T R :  P A T E N T  T R A N S L A T I O N  R E S O U R C E
+
+Download link: 	http://www.cl.uni-heidelberg.de/statnlpgroup/pattr/
+Author: 	Katharina Wäschle (waeschle@cl.uni-heidelberg.de)
+Date: 		28/02/2013
+
+PatTR is a parallel corpus extracted from documents in the MAREC patent
+collection [1]. The first release contains 23 million German-English
+parallel sentences collected from all patent text sections.
+
+
+0. TERMS OF USE
+
+ PatTR is licensed under a Creative Commons Attribution-NonCommercial-
+ ShareAlike 3.0 Unported License (see LICENSE). Please cite
+ Wäschle & Riezler (2012b), if you use the corpus in your work.
+
+1. FILES
+
+	abstract/
+		pattr.de-en.abstract.de
+		pattr.de-en.abstract.en
+		pattr.de-en.abstract.meta
+
+	claims/
+		pattr.de-en.claims.de
+		pattr.de-en.claims.en
+		pattr.de-en.claims.meta
+
+	description/
+		pattr.de-en.description.de
+		pattr.de-en.description.de.meta
+		pattr.de-en.description.en
+		pattr.de-en.description.en.meta
+
+	title/
+		pattr.de-en.title.de
+		pattr.de-en.title.en
+		pattr.de-en.title.meta
+
+ *.de files contain German sentences, *.en files corresponding English
+ sentences. *.meta contain information about the document the 
+ sentences were extracted from as tab-separated values:
+
+	- document id
+	- patent family id
+	- publication data
+	- IPC up to subclass level, comma-separated
+
+ For the description data, where the bitext has been collected from two
+ separate documents, there is a metadata file for each of the source
+ documents (*.de.meta for the German document from the EPO corpus,
+ *.en.meta for the English document from the USPTO corpus).
+
+2. DATA
+
+ The corpus is split into files according to the text sections of a
+ patent document: title, abstract, claims and description.
+ Parallel data from the title, abstract and claims sections were
+ extracted from documents belonging to the European Patent Office
+ (EPO) [2] and the World Intellectual Property Organization (WIPO) [3]
+ corpora in MAREC. Both resources feature multilingual documents that
+ contain for example both an English and a German abstract.
+
+ Since there are no multilingual descriptions, data from this section
+ were collected by exploiting patent families to align German documents
+ from the EPO corpus to English documents from the United States Patent
+ and Trademark Office (USPTO) [4] corpus, following Utiyama and Isahara
+ (2007).
+
+ All sections were sentence-aligned using the Gargantua aligner [5].
+ Preprocessing was done automatically. Sentence boundaries were detected
+ using the Europarl processing tools [6].
+
+4. STATISTICS
+
+ Section     Sentences  EN tokens     DE tokens
+
+ title        2,101,107    16,457,527  13,212,645
+ abstract       720,571    30,942,571  26,803,868
+ claims       8,346,863   501,373,533 435,117,827
+ description 11,829,816   498,948,414 386,920,744
+ total       22,998,357 1,047,722,045 862,055,084
+
+5. TEST SETS
+
+ The training and test sets used in Wäschle & Riezler (2012a) can be
+ provided on request to waeschle@cl.uni-heidelberg.de. For creating
+ custom training and test sets, an easy option is to split the corpus by
+ document publication date. Note, that abstract and claims data contain
+ a small amount (less than 1%) of duplicate and near-duplicate sentences
+ due to multiple instances of the same patent document in the two
+ corpora. To prevent overlap, make sure family ids of test and training
+ set are disjunct. Furthermore, about 7% of the description data are
+ duplicates. This is caused by the patent writing process, where whole
+ paragraphs are copied verbatim from other documents, e.g. when parts of
+ an invention are similar to a previously filed one. These documents do
+ not share a patent id, so they cannot be easily identified. Indicators
+ are mutual citations and documents filed by the same company. We did
+ not remove these duplicates because they are a feature of patent
+ corpora. Since patent titles are very short and general, 15% of title
+ data are natural duplicates.
+
+6. ACKNOWLEDGEMENTS
+
+ The work was in part supported by the "Cross-language Learning-to-Rank
+ for Patent Retrieval" project funded by the Deutsche
+ Forschungsgemeinschaft (DFG).
+
+PUBLICATIONS
+
+ Wäschle, K. and Riezler, S. (2012a). Structural and Topical Dimensions
+ in Multi-Task Patent Translation. Proceedings of the 13th Conference of
+ the European Chapter of the Association for Computational Linguistics
+ (EACL 2012), Avignon, France. 
+ http://www.aclweb.org/anthology-new/E/E12/E12-1083.pdf
+
+ Wäschle, K. and Riezler, S. (2012b). Analyzing Parallelism and Domain
+ Similarities in the MAREC Patent Corpus. Multidisciplinary Information
+ Retrieval, pp. 12-27.
+ http://www.cl.uni-heidelberg.de/~riezler/publications/papers/IRF2012.pdf
+
+LINKS
+     1. http://www.ir-facility.org/prototypes/marec
+     2. http://www.epo.org
+     3. http://www.wipo.int
+     4. http://www.uspto.gov
+     5. http://sourceforge.net/projects/gargantua
+     6. http://www.statmt.org/europarl

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