Author Attribution

Taiga is a corpus, where text sources and their meta-information are collected according to popular ML tasks. Each text in corpus is represented in plain text and with morphological and syntactic annotation (UDPipe, homonymy resolved automatically) + has metainformation - date, theme, authorship, text difficulcy…etc (depending on source) By now, about 5 billions of words are 77% literary texts (33 literary magazines), 19% of naive poetry, 2% of news (4 popular sites) and 2% of other (popular science, culture mags, social networks, amateur poems and prose), with documentation available. Segments Info | Genres | Tokens, millions | % | |------------------|------------------|-----| | News | 92 | 1.5 | | Literary Texts | 4605 | 76 | | Special datasets | 2.5 | 0.5 | | Social media | 80 | 1.5 | | Subtitles | 101 | 1.5 | | Poems | 1130 | 19 | **Annotation Example ** (CONLL-u): # newdoc # newpar # sent_id = 1 # 2003Armeniya.xml 1 # text = В советский период времени число ИТ- специалистов в Армении составляло около десяти тысяч. # sent_id = 1 1 В в ADP _ _ 3 case 3:case _ 2 советский советский ADJ _ Animacy=Inan|Case=Acc|Degree=Pos|Gender=Masc|Number=Sing 3 amod 3:amod _ 3 период период NOUN _ Animacy=Inan|Case=Acc|Gender=Masc|Number=Sing 11 obl 11:obl _ 4 времени время NOUN _ Animacy=Inan|Case=Gen|Gender=Neut|Number=Sing 3 nmod 3:nmod _ 5 число число NOUN _ Animacy=Inan|Case=Acc|Gender=Neut|Number=Sing 11 obj 11:obj _ 6 ИТ ит PROPN _ Animacy=Inan|Case=Nom|Gender=Neut|Number=Sing 8 compound 8:compound SpaceAfter=No 7 - - PUNCT _ _ 6 punct 6:punct _ 8 специалистов специалист NOUN _ Animacy=Anim|Case=Gen|Gender=Masc|Number=Plur 5 nmod 5:nmod _ 9 в в ADP _ _ 10 case 10:case _ 10 Армении армения PROPN _ Animacy=Inan|Case=Loc|Gender=Fem|Number=Sing 5 nmod 5:nmod _ 11 составляло составлять VERB _ Aspect=Imp|Gender=Neut|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act 0 root 0:root _ 12 около около ADP _ _ 14 case 14:case _ 13 десяти десять NUM _ Case=Gen 14 nummod 14:nummod _ 14 тысяч тысяча NOUN _ Animacy=Inan|Case=Gen|Gender=Fem|Number=Plur 11 nsubj 11:nsubj SpaceAfter=No 15 . . PUNCT _ _ 14 punct 14:punct _

Although research on author profiling has quite progressed in abundant resources languages, it is still infancy for limited resources languages such as Bengali. This repository contains our Bangla Author Profiling Dataset (BN-AuthProf). The primary objective is to introduce and benchmark the performance of machine learning approaches on Age and Gender Classification tasks from the social media status of people. BN-AuthProf dataset consists of 300 anonymized authors and 30,131 manually curated Facebook posts in Bangla, tagged with age and gender information.