Blackfoot Corpus Joel Dunham UBC Overview Blackfoot language - - PowerPoint PPT Presentation

Treebanking a Blackfoot Corpus

Joel Dunham UBC

Overview

• Blackfoot language
• Online Linguistic Database (OLD)
• Blackfoot OLD (BOLD)
• BOLD Annotation/treebanking

Blackfoot language

• Algonquian (Plains): Alberta & Montana
• Endangered: < 5000 speakers
• Fieldwork: UBC, UCalgary, UMontana

Blackfoot language

• Salient properties:
• Direct-inverse system
• Grammatical animacy
• Agglutinative

Blackfoot language

• Agglutinative:
• kimaaksawohpokooyimasi
• k-máak-sa-ohpook-ooyi-m-yii-wa-hsi
• 2-why-NEG-with-eat-TA-DIR-3SG-

CONJ

• „Why don‟t you eat with her?‟

OLD

• Online Linguistic Database
• www.onlinelinguisticdatabase.org
• Web application for documenting and

analyzing languages

OLD

• Open source (GPL): Python (Pylons),

MySQL, HTML/JS

• Powerful search capability: regex,

boolean

• Multi-user, web-based, collaborative
• Multi-media: audio, video, images, text
• Auto-linking of morphemes

Blackfoot OLD

• OLD web application for Blackfoot

(BLAOLD; funded by SSHRC)

• http://blaold.webfactional.com/
• Other OLD web apps:
• Okanagan OLD (OKAOLD)
• Plains Cree OLD (CRKOLD)
• etc.

BLAOLD

BLAOLD

• Forms (morphemes & sentences):

21,788 (2011-07-25)

• morphemes: 5,094
• sentences: 3,193
• unclassified: 13,501
• (word tokens: 20,577)

BLAOLD

• Sources:
• textual: 16,209 forms
• field work: 5,569 forms (and

growing...)

BLAOLD

• Collections
• texts created by ordered references to

forms

• 135 Collections at present
• E.g., Creation Story:
• http://blaold.webfactional.com/creati
• nstory

BLAOLD

• ...

Collection (text) created by referencing Forms entered into the BLAOLD.

BLAOLD

• Files:
• Associate Forms, Collections & Files
• 2,159 files (2011-07-25)
• 1,744 audio
• 259 image
• 148 text
• 4 video

Form with morphemic analysis Associated WAV file (tagged as an object language utterance) Associated JPG (used as a stimulus in elicitation) Morpheme segmentation and morpheme gloss lines. Blue text indicates links to morphemic Form entries found by the system POS string auto-generated: “prev-asp-vta drt-num nan drt-num agra-nan adt-asp- vai-oth-num”

BLAOLD: Goal

• Improve efficiency of data collection,

dissemination & analysis

• automate subtasks & improve search
• morphological parsing
• treebanking?

Morphological Parser

• „A morphological parser for Blackfoot‟

(Dunham, 2010; WAIL)

• input = transcription:
• kimaaksawohpokooyimasi
• utput = <segmentation, morph glosses, POSes>:
• k-máak-sa-ohpook-ooyi-m-yii-wa-hsi
• 2-why-NEG-with-eat-TA-DIR-3SG-CONJ
• agra-adt-oth-adt-vai-fin-thm-agrb-agrb

Morphological Parser

Phonology Morphotactics (lexicon)

FST

kimaaksawohpokooyimasi

k-máak-sa-ohpook-ooyi-m-yii-wa-hsi 2-why-NEG-with-eat-TA-DIR-3SG-CONJ agra-adt-oth-adt-vai-fin-thm-agrb-agrb

Morphotactics & lexicon extracted programmatically from the BLAOLD Phonology (from a grammar) hand-coded into FST Accuracy: ca. 70% Challenges:

• variations in transcription
• no hard and fast spelling

rules

• researchers differ in the

extent to which they use the standard phonemic

• rthography to capture

phonetic detail POS/morphemic N-grams used to select most probable parse

Morphological Parser

• Benefits of a morphological parse(r):
• parse online in real time (i.e., during data

entry): save researcher time

• create more data to improve searching

Morphological Parser

• Search example: find all sentences with

an overt subject and an overt object

• Regex on POS string for 2 nominal

roots:

• /n[ai][nr].*n[ai][nr].*/

Morphological Parser

/n[ai][nr].*n[ai][nr].*/ Good Bad

Treebank

NP VBD NP DT VP NP S (S (NP (DT oma) (NP aakííwa)) (VP (VBD iihpóma) (NP ónnikii))) TGrep: „S < (NP $. (VP < NP))‟

Treebank

• Assuming a flat morphological structure, the

syntactic phrase structure parsing of Blackfoot may actually be easy relative to English

• one of the longest words in the BLAOLD by

character (69 chr.s) has only 5 words

Treebank

ann-wa á'p-á-istot-i-m om-yi náápi-moyis ki saaki-á'p-á-istot-i-m-wa-áyi drt-num adt-asp-fin-fin-thm drt-num nan-nin und adt-adt-asp-fin-fin-thm-agrb

• th

DEM

„He is building that house and he is still building it.‟

VBZ DEM NN VBZ CC NP NP VP S VP S S

Treebank

• Worth it to treebank Blackfoot?

Cons Pros lots of researcher hours & money might significantly improve search :. research efficiency time might be better spent elsewhere, e.g., elicitation automated parsing may be relatively easy

Nitsííkoohtaahsi‟taki