Wajinimi/Children_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

The dataset reflects naturalistic child language, resulting in class imbalance typical of real-world conversational data.

Example distribution:

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

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

\[ Po = \frac{\sum C{ii}}{N} \]

Where:

• —\(C_{ii}\) = number of rows where annotators assigned the same category
• —\(N\) = total number of annotated rows

Cohen's Kappa

\[ \kappa = \frac{Po - Pe}{1 - P_e} \]

Where expected agreement is defined as:

\[ Pe = \sum \left(\frac{Ri}{N} \cdot \frac{C_i}{N}\right) \]

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: Yes, Okay, Yeah

Developmental Linguistic Features

The dataset preserves several characteristics typical of early child language.

Telegraphic speech refers to utterances dominated by content words while omitting grammatical elements.

Examples: Want Juice, Doggy Running, Baby Sleep

Preserving these patterns allows models trained on this dataset to better interpret non-standard developmental speech.

Intended Use

The dataset is intended for research in:

• —Child-centred NLP
• —Intent classification
• —Developmental linguistics
• —Child-robot interaction
• —Conversational AI for children
• —Human–AI interaction in early childhood environments