Alberts, I.; Forest, D.: Email pragmatics and automatic classification : a study in the organizational context (2012)
0.01
0.013872546 = product of:
0.027745092 = sum of:
0.027745092 = product of:
0.055490185 = sum of:
0.055490185 = weight(_text_:k in 238) [ClassicSimilarity], result of:
0.055490185 = score(doc=238,freq=6.0), product of:
0.16245733 = queryWeight, product of:
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.045509085 = queryNorm
0.34156775 = fieldWeight in 238, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.569778 = idf(docFreq=3384, maxDocs=44218)
0.0390625 = fieldNorm(doc=238)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- This paper presents a two-phased research project aiming to improve email triage for public administration managers. The first phase developed a typology of email classification patterns through a qualitative study involving 34 participants. Inspired by the fields of pragmatics and speech act theory, this typology comprising four top level categories and 13 subcategories represents the typical email triage behaviors of managers in an organizational context. The second study phase was conducted on a corpus of 1,703 messages using email samples of two managers. Using the k-NN (k-nearest neighbor) algorithm, statistical treatments automatically classified the email according to lexical and nonlexical features representative of managers' triage patterns. The automatic classification of email according to the lexicon of the messages was found to be substantially more efficient when k = 2 and n = 2,000. For four categories, the average recall rate was 94.32%, the average precision rate was 94.50%, and the accuracy rate was 94.54%. For 13 categories, the average recall rate was 91.09%, the average precision rate was 84.18%, and the accuracy rate was 88.70%. It appears that a message's nonlexical features are also deeply influenced by email pragmatics. Features related to the recipient and the sender were the most relevant for characterizing email.
