The first version of this project.

README.md

@@ -1,2 +1,37 @@
 # SE-WRL
-Improve word representation learning with sememes.
+The code for **Improve word representation learning with sememes**(ACL2017).
+
+## How to Run
+
+Using the following command to train word-sense-sememe embeddings.
+
+```sh
+cp SSA.c[SSA.c/MST.c/SAC.c/SAT.c] word2vec/word2vec.c
+cd word2vec
+make
+./word2vec -train TrainFile -output vectors.bin -cbow 0 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 30 -binary 1 -iter 1 -read-vocab VocabFile -read-meaning SememeFile -read-sense Word_Sense_Sememe_File -min-count 1
+```
+
+``TrainFile`` is train data set. ``VocabFile`` is the word vocabulary file, and ``SememeFile`` is the sememe vocabulary file. ``Word_Sense_Sememe_File`` is a file recording group information of word-sense-sememe.
+
+Before training, you should replace ``word2vec/word2vec.c`` with one of the four files ``SSA.c/MST.c/SAC.c/SAT.c``.
+
+## Data Set
+
+``HowNet.txt`` is an Chinese knowledge base with annotated word-sense-sememe information.
+
+``Sougo-T(sample).txt`` is a sample dataset extracted from ``Sougo-T``.
+
+## Evaluation Set
+
+``wordsim-240.txt`` and ``wordsim-297.txt`` in this files are utilized to evaluate the quality of word representations.
+
+``analogy.txt`` in this file is utilized to evaluate models' capability of word analogy inference.
+
+## Annotation Information
+
+The annotation information is for the four files ``SSA.c/MST.c/SAC.c/SAT.c``. Annotation of the common code is only included in file ``SSA.c``.
+
+## TODO
+
+Because of the size of train data is too large, I do not upload it. In the future, some eclectic measure will be conducted.