Explain hard/soft event schema design and add a complete earthquake example

README.md

@@ -29,12 +29,27 @@ configs:
 
 **DocEE** is a benchmark for extracting events and their arguments from full news documents, introduced by Tong et al. at NAACL 2022. Its annotations describe the main event in each document and the people, organizations, locations, times, and other event-specific information associated with it. Arguments can occur across multiple sentences, making the dataset suitable for studying document-level understanding. See the [paper](https://aclanthology.org/2022.naacl-main.291/) for the dataset construction process and original experiments.
 
-The English ontology contains **59 event types**, covering both hard news and soft news. Example types include `Earthquakes`, `Road Crash`, `Diplomatic Visit`, and `Awards Ceremony`. Each event type has its own argument schema.
+The English ontology contains **59 event types** and **356 distinct argument types**. The event types comprise **31 hard-news types** and **28 soft-news types**, with a dedicated argument schema for each event type.
 
 This dataset reformats the official English [DocEE dataset](https://github.com/tongmeihan1995/DocEE), using its **normal_setting** train/dev/test files. It contains one main event per document and preserves the original split membership and retained row order. The upstream `dev` split is named `validation` here. Chinese data and the cross-domain setting are not included.
 
 This Hugging Face release provides **27,478 documents**, with all roles from the corresponding event schema included in every record.
 
+## How the event schema is designed
+
+DocEE draws on the distinction between **hard news** and **soft news** in journalism:
+
+| Event group | Number of event types | Focus | Examples |
+|---|---:|---|---|
+| Hard news | 31 | Urgent events with immediate public consequences | Earthquakes, road crashes, armed conflicts |
+| Soft news | 28 | Human-interest, celebrity, sports, and entertainment stories | Celebrity marriages, sports competitions, awards ceremonies |
+
+These are broad groups used to design the ontology; each document receives one of the **59 specific event labels**. See the [original repository](https://github.com/tongmeihan1995/DocEE) and [paper, Section 3.1](https://aclanthology.org/2022.naacl-main.291.pdf).
+
+For each event type, the authors first examined **20 Wikipedia event pages** and used shared infobox keys as candidate argument roles. They then collected **20 New York Times stories** of that type. **Five journalism students** identified additional facts readers would want to know; their suggestions were merged, trial annotation was conducted, and rarely occurring roles were filtered out. This produces both common roles, such as dates, and event-specific roles, such as earthquake magnitude or flood water level. [Schema construction details](https://aclanthology.org/2022.naacl-main.291.pdf).
+
+An event schema therefore specifies **which kinds of information to extract for a given event type**. The ontology has 356 distinct role names overall; each event uses only its own subset. For example, the released `Earthquakes` schema has **15 roles**, including `Epicenter`, `Magnitude`, `Affected Area`, and `Casualties and Losses`. The complete role lists are available in [schemas.json](schemas.json).
+
 ## Supported tasks
 
 - **Event classification:** predict the main event type from the event trigger and document.
@@ -50,8 +65,8 @@ The `event_type` field and `arguments` are gold annotations. For event classific
 | Language | English |
 | Setting | Normal |
 | Documents / main events | 27,478 |
-| Event types | 59 |
-| Distinct argument-role names | 356 |
+| Event types | 59 (31 hard news + 28 soft news) |
+| Argument types (distinct role names) | 356 |
 | Argument text values | 228,692 |
 
 Argument values are counted once per distinct text within each document and role.
@@ -78,6 +93,39 @@ Exactly four fields are provided per record:
 
 Every schema role appears exactly once. Unannotated roles have JSON `null` values, which become Python `None`. Annotated roles contain a list of distinct text values in first-occurrence order: identical strings within the same document and role are included only once. `schemas.json` provides the complete event ontology.
 
+## Example: an earthquake event
+
+The following is a complete record from the **test split**, about the 1933 Diexi earthquake. Its document text and annotations are reproduced as stored in this release. It demonstrates the four fields and **all 15 roles** in the `Earthquakes` schema.
+
+```json
+{
+  "event_trigger": "1933 Diexi earthquake",
+  "document": "The 1933 Diexi earthquake occurred in Diexi, Mao County, Szechwan, Republic of China on August 25 with a moment magnitude of 7.3 and a maximum Mercalli intensity of X (Extreme). With up to 9,300 killed, this was the deadliest earthquake of 1933. This earthquake destroyed the town of Diexi and surrounding villages, and caused many landslides, and killed about 9,000 people. [4] The old town of Diexi sank into the landslide dam-created Diexi Lake. ",
+  "event_type": "Earthquakes",
+  "arguments": [
+    {"role": "Number of Rebuilding House", "values": null},
+    {"role": "Number of Destroyed Building", "values": null},
+    {"role": "Economic Loss", "values": null},
+    {"role": "Epicenter", "values": null},
+    {"role": "Number of Trapped People", "values": null},
+    {"role": "Number of Aftershocks", "values": null},
+    {"role": "Aid Agency", "values": null},
+    {"role": "Number of Evacuated People", "values": null},
+    {"role": "Casualties and Losses", "values": ["up to 9,300 killed"]},
+    {"role": "Affected Area", "values": ["Diexi, Mao County, Szechwan, Republic of China"]},
+    {"role": "Temporary Settlement", "values": null},
+    {"role": "Date", "values": ["1933", "August 25"]},
+    {"role": "Number of Rescued People", "values": null},
+    {"role": "Magnitude", "values": ["7.3"]},
+    {"role": "Aid Supplies/Amount", "values": null}
+  ]
+}
+```
+
+Here, `event_trigger` is the source title and `event_type` is the specific class `Earthquakes`, a hard-news event type. `Magnitude` has one annotated value, while `Date` has two. Roles without an annotation, such as `Economic Loss`, remain present with `values: null` (Python `None`). **Null means that this role has no gold annotation; it does not establish that the corresponding fact is absent in the real world.** Values are distinct text strings within each document and role.
+
+The record does not contain a separate `schema` field: the role names are listed in `arguments`, and the reusable ontology is provided in `schemas.json`.
+
 ## Usage
 
 ```python