fix DOI in DESCRIPTION with new format

Author: jwijffels

DESCRIPTION

@@ -1,15 +1,15 @@
 Package: doc2vec
 Type: Package
 Title: Distributed Representations of Sentences, Documents and Topics
-Version: 0.2.1
+Version: 0.2.2
 Authors@R: c(
     person('Jan', 'Wijffels', role = c('aut', 'cre', 'cph'), email = '[email protected]', comment = "R wrapper"), 
     person('BNOSAC', role = 'cph', comment = "R wrapper"),
     person('hiyijian', role = c('ctb', 'cph'), comment = "Code in src/doc2vec"))
 Maintainer: Jan Wijffels <[email protected]>
 Description: Learn vector representations of sentences, paragraphs or documents by using the 'Paragraph Vector' algorithms,
     namely the distributed bag of words ('PV-DBOW') and the distributed memory ('PV-DM') model. 
-    The techniques in the package are detailed in the paper "Distributed Representations of Sentences and Documents" by Mikolov et al. (2014), available at <arXiv:1405.4053>.
+    The techniques in the package are detailed in the paper "Distributed Representations of Sentences and Documents" by Mikolov et al. (2014), available at <doi:10.48550/arXiv.1405.4053>.
     The package also provides an implementation to cluster documents based on these embedding using a technique called top2vec. 
     Top2vec finds clusters in text documents by combining techniques to embed documents and words and density-based clustering.
     It does this by embedding documents in the semantic space as defined by the 'doc2vec' algorithm. Next it maps
@@ -18,12 +18,12 @@ Description: Learn vector representations of sentences, paragraphs or documents
     areas are the topic clusters which can be represented by the corresponding topic vector which is an aggregate of the 
     document embeddings of the documents which are part of that topic cluster. In the same semantic space similar words can 
     be found which are representative of the topic.
-    More details can be found in the paper 'Top2Vec: Distributed Representations of Topics' by D. Angelov available at <arXiv:2008.09470>. 
+    More details can be found in the paper 'Top2Vec: Distributed Representations of Topics' by D. Angelov available at <doi:10.48550/arXiv.2008.09470>. 
 URL: https://github.com/bnosac/doc2vec
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.1.1
+RoxygenNote: 7.1.2
 Depends: R (>= 2.10)
 Imports: Rcpp (>= 0.11.5), stats, utils
 LinkingTo: Rcpp

NEWS.md

@@ -1,3 +1,7 @@
+## CHANGES IN doc2vec VERSION 0.2.2
+
+- Fix DOI in DESCRIPTION
+
 ## CHANGES IN doc2vec VERSION 0.2.1
 
 - Make sure words are only 100 characters when getting embeddings of documents (issue #20)