Update README.md

README.md

@@ -14,7 +14,7 @@ pretty_name: >-
   Semeval2018Task7 is a dataset that describes the Semantic Relation Extraction
   and Classification in Scientific Papers
 size_categories:
-- 100K<n<1M
+- 1K<n<10K
 source_datasets: []
 tags:
 - Relation Classification
@@ -22,10 +22,9 @@ tags:
 - Scientific papers
 - Research papers
 task_categories:
-- feature-extraction
+- text-classification
 task_ids:
-- semantic-similarity-classification
-- entity-linking-classification
+- entity-classification
 train-eval-index:
 - col_mapping:
     labels: tags
@@ -33,7 +32,7 @@ train-eval-index:
   config: default
   splits:
     eval_split: test
-  task: token-classification
+  task: text-classification
   task_id: entity_extraction
 ---
 # Dataset Card for SemEval2018Task7
@@ -78,14 +77,12 @@ The challenge focuses on domain-specific semantic relations and includes three d
 
 The three subtasks are:
 
-- Subtask 1.1: Relation classification on
-clean data
+- Subtask 1.1: Relation classification on clean data
   - In the training data, semantic relations are manually annotated between entities. 
   - In the test data, only entity annotations and unlabeled relation instances are given.
   - Given a scientific publication, The task is to predict the semantic relation between the entities.
   
-- Subtask 1.2: Relation classification on
-noisy data
+- Subtask 1.2: Relation classification on noisy data
   - Entity occurrences are automatically annotated in both the training and the test data.
   - The task is to predict the semantic
 relation between the entities. 
@@ -100,7 +97,7 @@ The following example shows a text snippet with the information provided in the
     Korean, a \<entity id=”H01-1041.10”>verb final language\</entity>with\<entity id=”H01-1041.11”>overt case markers\</entity>(...)
   - A relation instance is identified by the unique identifier of the entities in the pair, e.g.(H01-1041.10, H01-1041.11)
   - The information to be predicted is the relation class label: MODEL-FEATURE(H01-1041.10, H01-1041.11).
-
+For details, see the paper https://aclanthology.org/S18-1111/. 
 
 ### Supported Tasks and Leaderboards
 
@@ -220,26 +217,33 @@ An example of 'train' looks as follows:
 - `title`: the title of this abstract, a `string` feature
 - `abstract`: the abstract from the scientific papers, a `string` feature
 - `entities`: the entity id's for the key phrases, a `list` of entity id's.
+    - `id`: the instance id of this sentence, a `string` feature.
+    - `char_start`: the 0-based index of the entity starting, an `ìnt` feature.
+    - `char_end`: the 0-based index of the entity ending, an `ìnt` feature.
 - `relation`: the list of relations of this sentence marking the relation between the key phrases, a `list` of classification labels.
+    - `label`: the list of relations between the key phrases, a `list` of classification labels.
+    - `arg1`: the entity id of this key phrase, a `string` feature.
+    - `arg2`: the entity id of the related key phrase, a `string` feature.
+    - `reverse`: the reverse is `True` only if reverse is possible otherwise `False`, a `bool` feature.
 
+```python
+RELATIONS
+{"":0,"USAGE": 1, "RESULT": 2, "MODEL-FEATURE": 3, "PART_WHOLE": 4, "TOPIC": 5, "COMPARE": 6}
+```
 
 #### subtask_1_2
 - `id`: the instance id of this abstract, a `string` feature.
 - `title`: the title of this abstract, a `string` feature
 - `abstract`: the abstract from the scientific papers, a `string` feature
 - `entities`: the entity id's for the key phrases, a `list` of entity id's.
+    - `id`: the instance id of this sentence, a `string` feature.
+    - `char_start`: the 0-based index of the entity starting, an `ìnt` feature.
+    - `char_end`: the 0-based index of the entity ending, an `ìnt` feature.
 - `relation`: the list of relations of this sentence marking the relation between the key phrases, a `list` of classification labels.
-
-#### entities
-- `id`: the instance id of this sentence, a `string` feature.
-- `char_start`: the 0-based index of the entity starting, an `ìnt` feature.
-- `char_end`: the 0-based index of the entity ending, an `ìnt` feature.
-
-#### relation
-- `label`: the list of relations between the key phrases, a `list` of classification labels.
-- `arg1`: the entity id of this key phrase, a `string` feature.
-- `arg2`: the entity id of the related key phrase, a `string` feature.
-- `reverse`: the reverse is `True` only if reverse is possible otherwise `False`, a `bool` feature.
+    - `label`: the list of relations between the key phrases, a `list` of classification labels.
+    - `arg1`: the entity id of this key phrase, a `string` feature.
+    - `arg2`: the entity id of the related key phrase, a `string` feature.
+    - `reverse`: the reverse is `True` only if reverse is possible otherwise `False`, a `bool` feature.
 
 ```python
 RELATIONS