## 2 Experimental Methodology
### Text Feature Extraction
#### 2.3.1 File-Level Text Features  
Linguistics metrics were extracted from the automatically transcribed text. Features from the BERT family (BERT, DistilBERT, RoBERTa) and XLNet were extracted using the HuggingFace transformer [55] library. Keg of Text Analytics is an original feature. * **Keg of Text Analytics:** The file-level text features extracted using Matlab's text analytics toolbox include: total number of words (\(W_{t}\)); total number of unique words \(W_{u}\); unique words normalised (\(W_{u}/W_{t}\)) (looking out for repeated words or repetitive use of simple words); speech rate in words per seconds (\(W_{t}/t\)); number of words which are not'stop words' (\(W_{t}-W_{s}\)) ('stop words' are words which can be omitted without losing meaning of the sentence, e.g., 'a', 'the', 'to', 'and'); ratio of number of words with \(\geq\)4, \(\geq\)5 and \(\geq\)6 letters to the number of unique words (although the correlation between longer words and complexity is not constant in English, it aids in filtering out short words); the number of nouns, pronouns, adjectives, verbs, adverbs, auxiliary verbs, ad-positions, coordinating conjunctions, interjections, subordinating conjunctions and determiners and lastly, binary representation of the presence of the word \"cookie\" (included because many dementia subjects struggle to use the word \"cookie\" in the \"Cookie Theft\" picture). Figure 3 shows a typical parts-of-speech comparison for AD and CN. Further, a word cloud of 45 randomly selected AD and CN subjects from training is shown in Figure 4 (words with fewer than 4 characters were ignored in these charts). The difference in word count between CN and AD is obvious (seen in both Figures 3 and 4 ): the CN group has a larger vocabulary bank than the AD group. Furthermore, CN subjects produce longer words frequently, indicating a possibly higher cognitive capacity. Two sets of file-level text features were investigated: Keg of Text Analytics and Keg of Text Analytics-Extended. In the former, a total of 12 features were used (\(W_{t},W_{u}\), ratio of number of words with more than \(\geq\)4, \(\geq\)5 and \(\geq\)6 letters to the number of unique words, \(W_{t}-W_{s}\), \(W_{t}/t\), number of pronouns, nouns, adverbs, adjectives and auxiliary verbs), and in the latter, a super-set with 18 features (containing the former 12 plus 6 additional features: number of ad-positions, coordinating conjunctions, interjections, subordinating conjunctions and determiners and binary representation of presence of \"cookie\") was used. Figure 3: A typical parts-of-speech comparison extracted as part of Keg of Text Analytics. Differences between Alzheimer’s Dementia (AD) and Cognitively Normal (CN) subjects can be observed, particularly for nouns. * **Summed Word Embedding:** Words from the transcripts are embedded into a vector space model using FastText pretrained word embedding and sentence classification [56,57]. The hyperspace dimension for this embedding was 300, resulting in a vector of 300 elements for each word. The resulting vectors were then summed up to produce an overall representation of the transcription. Although the implication of this sum is not immediately intuitive, our results here suggest that, at least for this dataset, it seems helpful to provide cues for the overall transcript. * **BERT:** The text transcriptions were used to generate feature embedding vectors from the pretrained BERT (Bidirectional Encoder Representations from Transformers) [14] model. BERT is used for pretraining a language representation over a large amount of unlabeled textual data and employs an attention mechanism (an encoder that reads the text and a decoder that predicts), resulting in deeper sense of language contextual relationships between words in a text. * **RoBERTa:** RoBERTa (a Robustly optimized BERT approach) [13] uses dynamic masking (unlike BERT's static masking) which increases the data variability (by augmentation) and helps in yielding more robust features.