Examples of passages in the text that was excluded were utterances regarding the time following the death of the older relative such as the arrangements for the funeral or the next of kin's own situation and grief following the loss of a relative.
We will now explain the main choices we made for our system regarding utterance selection and utterance verification procedures.
We provide a computational model of number interpretation as social inference regarding the communicative goal, meaning, and affective subtext of an utterance.
It does not seem that an utterance is recognizable as an assertion in virtue of a decision to regard it as subject to this or that norm.
If we suppose that a given target language's entire set of utterances is regarded as having been produced by different languages, a common pool of hidden variables acts as basis factors and represents the utterances from different languages.
In such cases an utterance is already apt, already in complete accord with pertinent conversational norms, when regarded as an effort to mean what one says, no more and no less.
In this regard the proposition and the sentence mood of an utterance are supposed to be encoded in a predetermined sequence across all sentence types and all languages.
However, in the total variability space estimation, a given speaker's entire set of utterances is regarded as having been produced by different speakers.
We estimate the language total variability space by using the dataset shown in Section 5, and we suppose that a given target language's entire set of utterances is regarded as having been belonging to different language.
Regarding utterance contents, three dictionaries were constructed: medical, emotional, and social.
Regarding utterance types, communication used during consultations was analyzed with the dictionaries included in the LaComm.
