init

README.md

@@ -19,3 +19,56 @@ pretty_name: Lexical Relation Classification
 ### Dataset Summary
 Five different datasets (`BLESS`, `CogALexV`, `EVALution`, `K&H+N`, `ROOT09`) for lexical relation classification used in [SphereRE](https://www.aclweb.org/anthology/P19-1169/).
 
+
+### Dataset Summary
+This dataset contains 5 different word analogy questions used in [Analogy Language Model](https://aclanthology.org/2021.acl-long.280/).
+
+| name | Size (valid/test) | Num of choice | Num of relation group | Original Reference                                                         |
+|---------|------------------:|--------------:|----------------------:|:--------------------------------------------------------------------------:|
+| `sat_full`| -/374             | 5             | 2                     | [Turney (2005)](https://arxiv.org/pdf/cs/0508053.pdf)                      |
+| `sat`     | 37/337            | 5             | 2                     | [Turney (2005)](https://arxiv.org/pdf/cs/0508053.pdf)                      |
+| `u2`      | 24/228            | 5,4,3         | 9                     | [EnglishForEveryone](https://englishforeveryone.org/Topics/Analogies.html) |
+| `u4`      | 48/432            | 5,4,3         | 5                     | [EnglishForEveryone](https://englishforeveryone.org/Topics/Analogies.html) |
+| `google`  | 50/500            | 4             | 2                     | [Mikolov et al., (2013)](https://www.aclweb.org/anthology/N13-1090.pdf)    |
+| `bats`    | 199/1799          | 4             | 3                     | [Gladkova et al., (2016)](https://www.aclweb.org/anthology/N18-2017.pdf)   |
+
+
+## Dataset Structure
+### Data Instances
+An example of `test` looks as follows.
+```
+{
+    "stem": ["raphael", "painter"],
+    "answer": 2,
+    "choice": [["andersen", "plato"],
+                ["reading", "berkshire"],
+                ["marx", "philosopher"],
+                ["tolstoi", "edison"]]
+}
+```
+where `stem` is the query word pair, `choice` has word pair candidates,
+and `answer` indicates the index of correct candidate which starts from `0`.
+All data is lowercased except Google dataset.
+
+### Citation Information
+```
+@inproceedings{wang-etal-2019-spherere,
+    title = "{S}phere{RE}: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings",
+    author = "Wang, Chengyu  and
+      He, Xiaofeng  and
+      Zhou, Aoying",
+    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2019",
+    address = "Florence, Italy",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/P19-1169",
+    doi = "10.18653/v1/P19-1169",
+    pages = "1727--1737",
+    abstract = "Lexical relations describe how meanings of terms relate to each other. Typical examples include hypernymy, synonymy, meronymy, etc. Automatic distinction of lexical relations is vital for NLP applications, and also challenging due to the lack of contextual signals to discriminate between such relations. In this work, we present a neural representation learning model to distinguish lexical relations among term pairs based on Hyperspherical Relation Embeddings (SphereRE). Rather than learning embeddings for individual terms, the model learns representations of relation triples by mapping them to the hyperspherical embedding space, where relation triples of different lexical relations are well separated. Experiments over several benchmarks confirm SphereRE outperforms state-of-the-arts.",
+}
+```
+
+### LICENSE
+The LICENSE of all the resources are under [CC-BY-NC-4.0](./LICENSE). Thus, they are freely available for academic purpose or individual research, but restricted for commercial use.
+

lexical_relation_classification.py

@@ -40,7 +40,7 @@ _URLS = {
             'EVALution': [f'{_URL}/EVALution/test.jsonl'],
         },
     str(datasets.Split.VALIDATION): {
-            'BLESS': [f'{_URL}/bats/val.jsonl'],
+            'BLESS': [f'{_URL}/BLESS/val.jsonl'],
             'K&H+N': [f'{_URL}/K&H+N/val.jsonl'],
             'ROOT09': [f'{_URL}/ROOT09/val.jsonl'],
             'EVALution': [f'{_URL}/EVALution/val.jsonl'],