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Diffstat (limited to 'tensorflow/g3doc/tutorials/word2vec/index.md')
| -rw-r--r-- | tensorflow/g3doc/tutorials/word2vec/index.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/tensorflow/g3doc/tutorials/word2vec/index.md b/tensorflow/g3doc/tutorials/word2vec/index.md index 34f3d12f89..a9cc51207d 100644 --- a/tensorflow/g3doc/tutorials/word2vec/index.md +++ b/tensorflow/g3doc/tutorials/word2vec/index.md @@ -227,7 +227,7 @@ When we inspect these visualizations it becomes apparent that the vectors capture some general, and in fact quite useful, semantic information about words and their relationships to one another. It was very interesting when we first discovered that certain directions in the induced vector space specialize -towards certain semantic relationships, e.g. *male-female*, *gender* and +towards certain semantic relationships, e.g. *male-female*, *verb tense* and even *country-capital* relationships between words, as illustrated in the figure below (see also for example [Mikolov et al., 2013](http://www.aclweb.org/anthology/N13-1090)). |