Update README.md

README.md

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 ---
-license: ecl-2.0
-size_categories:
-- 10K<n<100K
----
+language:
+- en
+license: cc-by-4.0
+pretty_name: MAMA Communicative Intent Dataset (INCA-A Annotated)
+tags:
+- NLP
+- child-language
+- intent-classification
+- conversational-ai
+- developmental-linguistics
+- dialogue
+- child-speech
+task_categories:
+- text-classification
+task_ids:
+- intent-classification
+---
+
+# MAMA Communicative Intent Dataset (INCA-A Annotated)
+
+## Overview
+
+The **MAMA Communicative Intent Dataset** is a linguistically annotated corpus of child utterances designed to support research in **child-centred Natural Language Processing (NLP)** and **communicative intent recognition in early language development**.
+
+The dataset contains **10,800 child utterances** annotated using the **INCA Communicative Coding System** (Ninio et al., 1994), a developmental framework that identifies the communicative functions underlying children's speech.
+
+To make the dataset suitable for machine learning, the original INCA codes were mapped to **23 refined intent categories** representing distinct communicative behaviours in early child language.
+
+This dataset was created as part of the **MAMA (Machine-Assisted Maternal Assistant)** research project, which investigates how artificial intelligence systems can better understand the communicative behaviour of young children.
+
+Unlike many NLP corpora that normalise or correct non-standard language, this dataset **preserves authentic developmental linguistic features**, including telegraphic speech and missing grammatical markers.
+
+---
+
+# Dataset Summary
+
+| Property | Value |
+|--------|------|
+| Total utterances | 10,800 |
+| Total labelled instances | 11,410 |
+| Intent categories | 23 |
+| Annotation framework | INCA Communicative Coding System |
+| Source corpus | CHILDES |
+| Language | English |
+| Task | Intent Classification |
+
+The dataset reflects **naturalistic child language**, resulting in class imbalance typical of real-world conversational data.
+
+Example distribution:
+
+| Intent | Frequency |
+|------|------|
+| Observation / Reference | 2,964 |
+| Narrative / Storytelling | 1,664 |
+| Comfort | 4 |
+
+---
+
+# Annotation Framework
+
+The dataset is based on the **INCA Communicative Coding System**, introduced in:
+
+> Ninio, A., Snow, C., Pan, B., & Rollins, P. (1994).  
+> *Classifying communicative acts in children's interactions.*
+
+The INCA system categorises **communicative functions** in children's speech rather than grammatical structure alone.
+
+In this dataset, INCA codes were mapped into **refined NLP intent categories** suitable for supervised machine learning.
+
+---
+
+# Mapping from INCA Codes to Refined Intent Categories
+
+| INCA Category | INCA Code | Refined Intent Category |
+|---------------|----------|-------------------------|
+| Directing hearer’s attention | DHA / CL | Attention |
+| Speech elicitation | EI, RT, EA | Imitation |
+| Questions | QN, YQ, TQ | Question |
+| Evaluation | ET | Excitement |
+| Discussing related-to-present | DRP | Narrative or Storytelling |
+| Discussing joint focus | DJF | Observation or Reference |
+| Statements | WS | Desire or Action |
+| Negotiating activity | NIA / DW | Disagreement or Correction |
+| Marking | MRK | Gratitude |
+| Comforting | CMO | Comfort |
+| Directiveness | RP | Request |
+| Directiveness | RD, CS | Refusal |
+| Directiveness | GR | Explanation or Justification |
+| Declaration | YD / AP | Agreement or Acknowledgment |
+| Marking | MK | Greeting |
+| Marking | EM | Distress or Pain |
+| Marking | EN | Emotion |
+| Fantasy discussion | DFW | Playtalk or Fantasy |
+| Possession negotiation | PSS | Possession |
+| Request / Suggest | RP | Need |
+| Dare / Challenge | DR | Command |
+| Disapprove / Protest | DS, ED, DW | Complaint |
+
+---
+
+# Annotation Protocol
+
+Annotation followed a **two-stage validation procedure**.
+
+### Stage 1 — Initial Annotation
+
+All utterances were initially labelled by the **primary researcher** using the INCA communicative coding framework.
+
+### Stage 2 — Expert Re-annotation
+
+To strengthen validity, the dataset was independently reviewed by two domain experts:
+
+- **Developmental Psychologist**
+- **Experienced Early-Years Teacher**
+
+This ensured both **developmental theoretical grounding** and **practical child-language expertise**.
+
+---
+
+# Inter-Annotator Reliability
+
+Agreement between annotators was measured using **Cohen's Kappa (κ)**.
+
+### Observed Agreement
+
+\[
+P_o = \frac{\sum C_{ii}}{N}
+\]
+
+Where:
+
+- \(C_{ii}\) = number of rows where annotators assigned the same category  
+- \(N\) = total number of annotated rows
+
+### Cohen's Kappa
+
+\[
+\kappa = \frac{P_o - P_e}{1 - P_e}
+\]
+
+Where expected agreement is defined as:
+
+\[
+P_e = \sum \left(\frac{R_i}{N} \cdot \frac{C_i}{N}\right)
+\]
+
+Where:
+
+- \(R_i\) = rows assigned to category \(i\) by annotator 1  
+- \(C_i\) = rows assigned to category \(i\) by annotator 2
+
+The resulting score was:
+
+**κ = 0.81**
+
+According to **Landis and Koch (1977)**, this represents **almost perfect agreement**, indicating strong reliability in intent categorisation.
+
+---
+
+# Linguistic Characteristics
+
+## Average Utterance Length
+
+Average utterance length was computed as:
+
+\[
+\text{Average utterance length} =
+\frac{\sum_{i=1}^{N} (\text{token length of utterance}_i)}{N}
+\]
+
+Analysis revealed that:
+
+- **Explanation or Justification**
+- **Narrative or Storytelling**
+- **Desire or Action**
+
+tend to produce **longer utterances**, indicating more verbose communicative behaviour.
+
+In contrast:
+
+- **Observation or Reference**
+
+typically contains **shorter utterances**, reflecting concise descriptions of objects or events in the shared environment.
+
+---
+
+## Lexical Diversity
+
+Lexical diversity analysis showed variation across communicative intents.
+
+Categories such as:
+
+- **Observation**
+- **Narrative or Storytelling**
+
+exhibited **higher vocabulary diversity**, reflecting descriptive language use.
+
+Conversely:
+
+- **Agreement or Acknowledgement**
+
+showed **low lexical diversity**, as these responses often rely on short, formulaic expressions such as: