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LAF Fabric

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Dirk Roorda
Dirk Roorda

Workbench for processing big LAF resources (Linguistic Annotation Framework)

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LAF Fabric Dirk Roorda DANS/TLA 2013-12-12 VU/ETCBC Amsterdam
What is LAF? Linguistic Annotation Framework ISO standard 24612:2012 Nancy Ide, Laurent Romary A data model for stand-off markup plus a serialization advice: GrAF
LAF examples OANC Open American National Corpus WIVU-ETCBC Text Database Hebrew Bible
OANC 17K 8 feb 2013 ch5-logical.xml 217K 8 feb 2013 ch5-mpqa.xml 265K 8 feb 2013 ch5-nc.xml 16K 8 feb 2013 ch5-ne.xml 1,3M 8 feb 2013 ch5-penn.xml 1,4M 8 feb 2013 ch5-ptb.xml 990K 8 feb 2013 ch5-ptbtok.xml 48K 8 feb 2013 ch5-s.xml 274K 8 feb 2013 ch5-seg.xml 177K 8 feb 2013 ch5-vc.xml 2,6K 3 jun 09:05 ch5.hdr 31K 8 feb 2013 ch5.txt 19K 8 feb 2013 resource-header.xml
text Semantics enters with purpose. 10 1-21 210 For this to be true, it is not necessary that the carriers of purpose, say, the same bacterium heading upstream in the glucose gradient, be conscious. I hope my definition of an autonomous agent is useful, an autocatalytic system carrying out a work cycle, now rather broadened by the realization that autonomous agents also do often detect and measure and record displacements of external systems from equilibrium that can be used to extract work, then do extract work, propagating work and constraint construction, from their environment.
annotations <region xml:id="mpqa-r64" anchors="2101 2110"/> ! * ... <node xml:id="mpqa-n64"> * <link targets="mpqa-r64"/> * </node> * <a xml:id="mpqa-N81257" label="target" ref="mpqa-n64" as="mpqa"> <fs> <f name="id" value="semantics"/> </fs> </a> !
headers Resource header Primary data header Annotation header Metadata, namespaces, annotation labels, statistics
SHEBANQ number_within_chapter=1 sentence sentence_atom clause_atom_number=1 clause_atom_relation=0 clause_atom_relation_daughter_tense=unknown clause_atom_relation_kind=No_relation clause_atom_relation_mother_tense=unknown clause_atom_relation_preposition_class=none clause_atom_type=xQtl indentation=0 clause_atom annotations (features) n_88917 clause n_28737 r_11 .. r_1 parents phrase parents phrase_atom n_40770 parents mother nodes n_object id p_7 regions 122r_monad number 123 n_12 r_10 r_11 w_6 106110-121 105 109 subphrase parents word n_11 <region xml:id="w_1" anchors="24 24"/> n_10 lexeme_utf8=‫ראשׁית‬ old_lexeme_utf8=‫ראשׁית‬ vocalized_lexeme_utf8=‫ראשׁית‬ ִ ֵ surface_consonants_utf8=‫ראשׁית‬ graphical_lexeme_utf8=‫ראשׁי‬ ִ֖ ֵ n_77637 pare parents parents nts word <region xml:id="r_2" anchors="6 23"/> <node xml:id="n_3"><link targets="r_2"/></node> <a xml:id="a_3" label="word" ref="n_3" as="monads"/> r_11 .. r_5 pare nts r_11 .. r_9 word <region xml:id="r_1" anchors="0 5"/> <node xml:id="n_2"><link targets="r_1"/></node> <a xml:id="a_2" label="word" ref="n_2" as="monads"/> n_59559 n_77638 primary text UNICODE-utf8 <a xml:id="a_f22" label="ft" ref="n_3" as="utf8"><fs> <f name="lexeme_utf8" value="‫>/"ראׁשית‬ <f name="old_lexeme_utf8" value="‫>/"ראׁשית‬ <f name="vocalized_lexeme_utf8" value="‫>/"ֵראׁשית‬ ִ <f name="surface_consonants_utf8" value="‫>/"ראׁשית‬ <f name="graphical_lexeme_utf8" value="‫>/"ֵראׁשי‬ ִ֖ </fs></a> parents labeled edges subphrase annotations (empty) <node xml:id="n_88917"> <link targets="r_1 r_2 r_3 r_4 r_5 r_6 r_7 r_8 r_9 r_10 r_11"/> </node> <a xml:id="a_88917" label="sentence_atom" ref="n_88917" as="lingo"/> <a xml:id="a_f71355" label="ft" ref="n_88917" as="lingo"><fs> <f name="sentence_atom_number" value="0"/> </fs></a> <edge xml:id="e_1" from="n_88917" to="n_84383"/> <a xml:id="a_e1" label="parents" ref="e_1" as="link"/> n_34680 r_11 .. r_5 link to regions n_84383 r_11 .. r_1 r_11 .. r_1 parents r_11 .. r_1 determination=determined is_apposition=false number_within_clause=4 phrase_function=Objc phrase_type=PP parent Linguistic Annotation Framework word n_9 r_9 r_8 w_5 97- 9392 104 96 r_7 .. r_5 pa parents parents re nts word word word n_8 n_7 r_7 72-91 r_6 6871 word n_6 w_4 r_5 67 59-66 word n_4 n_5 w_3 58 r_4 40-57 word w_2 39 r_3 25-38 word n_3 w_1 24 n_2 r_2 6-23 r_1 0-5 ‫בּראשׁית בּרא א.הים את השּׁמים ְואת הארץ׃‬ ֶ ָֽ ָ ֵ֥ ִ ַ֖ ָ ַ ֵ֥ ִ֑ ֱ ָ֣ ָ ִ֖ ֵ ְ
More about SHEBANQ data 2.27 GB XML 99.2% nodes 1,453,175 edges 1,524,637 regions 800,087 features 42,545,492 xml ids 12,831,550 words 426,499
LAF Processors http:/ /www.poio.eu
LAF Processors http:/ /www.exist-db.org
Performance Problems POIO: load time +60 min RAM +20 GB ExistDb: load time +30 min (initial) count features +60 min nodes/edges/features directly modeled as objects in Python ! ! xquery not a handy tool for relevant queries need extensive index building identifier chasing
LAF-Fabric http:/ /laf-fabric.readthedocs.org
What is it? a compiler LAF-XML ==> Python arrays 2,270 MB ==> 485 MB binary data 60 min load time ==> 1 s a task execution environment runs custom Python scripts offering them a LAF API
Where is it? clone it from Github https:/ /github.com/dirkroorda/laffabric run it locally share your custom tasks share your own annotations
Example: Esther linguistic variation among the bible books count the common nouns of Esther compare their freqs in Esther with those in other books of the Bible
Wanted: a tab separated file with frequencies for 216 common nouns for all books Genesis Exodus Lamentations Esther Daniel Ezra ‫אב‬ 27.8 3.31 2.94 2.27 6.95 9.1 ‫אבדנ‬ 0 0 0 1.51 0 0 ‫אבל‬ 0.534 0 1.47 1.51 1.16 0 ‫אגרת‬ 0 0 0 1.51 0 0 ‫אורה‬ 0 0 0 0.755 0 0 ‫אח‬ 23.8 2.34 0 0.755 0 6.82 ‫אחד‬ 6.28 13.6 0 3.78 7.34 6.82 ‫אחר‬ 11.4 3.86 0 1.51 1.93 2.27 ‫אחׁשדרפנימ‬ 0 0 0 2.27 0 0.569 ‫אי‬ 0.134 0 0 0.755 0.386 0 ‫אינ‬ 4.94 3.03 16.2 7.55 3.48 2.27
<a xml:id="amf21" label="ft" ref="n3"><fs> <f name="noun_type" value="common"/> <f name="part_of_speech" value="noun"/> <f name="phrase_dependent_part_of_speech" value="noun"/> <f name="pronoun_type" value="none"/> </fs></a> <a xml:id="amf33" label="ft" ref="n4"><fs> <f name="noun_type" value="none"/> <f name="part_of_speech" value="verb"/> <f name="phrase_dependent_part_of_speech" value="verb"/> <f name="pronoun_type" value="none"/> </fs></a> <a xml:id="amf45" label="ft" ref="n5"><fs> <f name="noun_type" value="common"/> <f name="part_of_speech" value="noun"/> <f name="phrase_dependent_part_of_speech" value="noun"/> <f name="pronoun_type" value="none"/> </fs></a> Given(1): LAF annotation files with part of speech features
Given (2) Information about the books (where they start and end) <region xml:id="s_1254379" anchors="4609273 4664382"/> <node xml:id="n1254379"><link targets="s_1254379"/></node> <a xml:id="as1254379" label="db" ref="n1254379"><fs> <f name="otype" value="book"/> <f name="oid" value="1254379"/> <f name="monads" value="368500-373120"/> <f name="minmonad" value="368500"/> <f name="maxmonad" value="373120"/> </fs></a> <a xml:id="asf34" label="sft" ref="n1254379"><fs> <f name="book" value="Esther"/> </fs></a>
a small python script to the e d workbench! co ce target_book = "Esther" ur for node in NN(): so this_type = F.shebanq_db_otype.v(node) if this_type == "word": p_o_s = F.shebanq_ft_part_of_speech.v(node) if p_o_s == "noun": noun_type = F.shebanq_ft_noun_type.v(node) if noun_type == "common": words[book_name] += 1 lexeme = F.shebanq_ft_lexeme_utf8.v(node) lexemes[book_name][lexeme] += 1 elif this_type == "book": book_name = F.shebanq_sft_book.v(node) books.append(book_name) ontarget = F.shebanq_sft_book.v(node) == target_book
Declare features "features": { "shebanq": { "node": [ "db.otype", "ft.part_of_speech,noun_type,lexeme_utf8", "sft.book", ], "edge": [ ], }, The workbench will load selected features unload other features
Receive task object def task(graftask): (msg, NN, F, X) = graftask.get_mappings() ! And use supplied methods for rapid data access.
run your task
‫‪Nah‬‬ ‫‪Obadiah Jonah‬‬ ‫‪Micah‬‬ ‫‪Joel‬‬ ‫‪Amos‬‬ ‫‪Jeremiah Ezekiel‬‬ ‫‪Hosea‬‬ ‫‪Judges‬‬ ‫‪I_SamuelII_Samuel‬‬ ‫‪I_Kings II_Kings Isaiah‬‬ ‫‪Genesis Exodus‬‬ ‫‪Leviticus Numbers Deuteronomy‬‬ ‫‪Joshua‬‬ ‫95.3‬ ‫0‬ ‫0‬ ‫7.2‬ ‫3.2‬ ‫41.1‬ ‫12.3‬ ‫49.7‬ ‫80.3‬ ‫6.51‬ ‫7.81‬ ‫26.7‬ ‫9.21‬ ‫3.71‬ ‫16.9‬ ‫4.31‬ ‫6.11‬ ‫67.4‬ ‫13.3‬ ‫8.72‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫8.1‬ ‫0‬ ‫0‬ ‫50.4‬ ‫0‬ ‫0‬ ‫911.0‬ ‫873.0‬ ‫492.0‬ ‫0‬ ‫0‬ ‫718.0‬ ‫342.0‬ ‫0‬ ‫0‬ ‫981.0‬ ‫0‬ ‫0‬ ‫0‬ ‫435.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫אגרת‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫אורה‬ ‫95.3‬ ‫0‬ ‫4.71‬ ‫7.2‬ ‫3.2‬ ‫24.3‬ ‫70.1‬ ‫93.2‬ ‫30.1‬ ‫31.1‬ ‫61.2‬ ‫52.9‬ ‫29.2‬ ‫18.8‬ ‫75.3‬ ‫80.9‬ ‫85.2‬ ‫83.4‬ ‫43.2‬ ‫8.32‬ ‫אח‬ ‫0‬ ‫55.4‬ ‫7.8‬ ‫67.6‬ ‫0‬ ‫41.1‬ ‫7.21‬ ‫67.1‬ ‫49.2‬ ‫74.7‬ ‫4.21‬ ‫35.9‬ ‫5.01‬ ‫2.8‬ ‫5.61‬ ‫29.4‬ ‫3.42‬ ‫33.9‬ ‫6.31‬ ‫82.6‬ ‫אחד‬ ‫0‬ ‫0‬ ‫0‬ ‫50.4‬ ‫5.11‬ ‫99.7‬ ‫89.2‬ ‫8.6‬ ‫74.1‬ ‫20.7‬ ‫11‬ ‫1.61‬ ‫9.21‬ ‫8.21‬ ‫11‬ ‫68.5‬ ‫49.3‬ ‫67.4‬ ‫68.3‬ ‫4.11‬ ‫אחר‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫70.1‬ ‫36.0‬ ‫97.2‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫431.0‬ ‫8.01‬ ‫0‬ ‫7.8‬ ‫67.6‬ ‫9.6‬ ‫1.71‬ ‫68.2‬ ‫2.11‬ ‫5.31‬ ‫35.4‬ ‫29.4‬ ‫80.4‬ ‫20.8‬ ‫2.8‬ ‫73.1‬ ‫86.5‬ ‫85.2‬ ‫4‬ ‫30.3‬ ‫49.4‬ ‫אינ‬ ‫4.41‬ ‫9.04‬ ‫1.62‬ ‫50.4‬ ‫2.9‬ ‫4.11‬ ‫6.01‬ ‫3.02‬ ‫4.9‬ ‫8.82‬ ‫7.61‬ ‫4.83‬ ‫3.15‬ ‫5.06‬ ‫8.91‬ ‫71‬ ‫8.71‬ ‫9.71‬ ‫2.31‬ ‫2.12‬ ‫איׁש‬ ‫95.3‬ ‫0‬ ‫0‬ ‫53.1‬ ‫0‬ ‫0‬ ‫67.4‬ ‫873.0‬ ‫30.1‬ ‫94.2‬ ‫31.4‬ ‫71.5‬ ‫18.6‬ ‫6.01‬ ‫92.3‬ ‫98.1‬ ‫1.41‬ ‫0‬ ‫25.1‬ ‫762.0‬ ‫אלפ‬ ‫8.1‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫75.4‬ ‫91.1‬ ‫31.1‬ ‫437.0‬ ‫89.4‬ ‫51.3‬ ‫718.0‬ ‫379.0‬ ‫80.6‬ ‫428.0‬ ‫64.2‬ ‫272.0‬ ‫68.2‬ ‫569.0‬ ‫74.3‬ ‫אמ‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫5.01‬ ‫405.0‬ ‫741.0‬ ‫976.0‬ ‫44.9‬ ‫36.1‬ ‫76.2‬ ‫806.0‬ ‫572.0‬ ‫80.2‬ ‫259.0‬ ‫175.0‬ ‫73.9‬ ‫47.1‬ ‫אמה‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫622.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫8.1‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫41.1‬ ‫832.0‬ ‫93.1‬ ‫67.1‬ ‫354.0‬ ‫389.0‬ ‫718.0‬ ‫342.0‬ ‫219.0‬ ‫428.0‬ ‫865.0‬ ‫0‬ ‫0‬ ‫672.0‬ ‫208.0‬ ‫אמת‬ ‫0‬ ‫55.4‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫832.0‬ ‫621.0‬ ‫44.0‬ ‫0‬ ‫393.0‬ ‫272.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫272.0‬ ‫0‬ ‫0‬ ‫431.0‬ ‫אפר‬ ‫0‬ ‫55.4‬ ‫0‬ ‫5.31‬ ‫0‬ ‫0‬ ‫19.6‬ ‫288.0‬ ‫44.0‬ ‫18.1‬ ‫92.6‬ ‫19.1‬ ‫59.1‬ ‫52.4‬ ‫76.4‬ ‫14.3‬ ‫57.7‬ ‫33.1‬ ‫84.6‬ ‫10.4‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫832.0‬ ‫621.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫403.0‬ ‫0‬ ‫0‬ ‫631.0‬ ‫0‬ ‫85.3‬ ‫0‬ ‫9.62‬ ‫90.9‬ ‫7.8‬ ‫1.13‬ ‫6.72‬ ‫8.22‬ ‫6.32‬ ‫1.43‬ ‫9.72‬ ‫1.61‬ ‫11‬ ‫9.01‬ ‫6.21‬ ‫2.81‬ ‫4.92‬ ‫3.73‬ ‫7.61‬ ‫6.51‬ ‫7.81‬ ‫5.14‬ ‫ארצ‬ ‫8.1‬ ‫0‬ ‫0‬ ‫7.2‬ ‫0‬ ‫17.5‬ ‫26.2‬ ‫45.4‬ ‫67.1‬ ‫3.4‬ ‫74.7‬ ‫3.31‬ ‫4.31‬ ‫12‬ ‫20.3‬ ‫59.7‬ ‫57.7‬ ‫7.41‬ ‫97.5‬ ‫3.02‬ ‫אׁשה‬ ‫0‬ ‫0‬ ‫0‬ ‫53.1‬ ‫3.2‬ ‫0‬ ‫76.1‬ ‫36.0‬ ‫60.2‬ ‫35.4‬ ‫787.0‬ ‫81.2‬ ‫379.0‬ ‫25.1‬ ‫0‬ ‫981.0‬ ‫27.2‬ ‫5.01‬ ‫71.3‬ ‫78.1‬ ‫בגד‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫41.1‬ ‫91.1‬ ‫252.0‬ ‫16.1‬ ‫31.1‬ ‫43.3‬ ‫54.2‬ ‫22.1‬ ‫37.2‬ ‫428.0‬ ‫7.1‬ ‫99.2‬ ‫84.2‬ ‫97.5‬ ‫78.1‬ ‫בד‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫בהט‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫911.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫בוצ‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫בזה‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫בזיונ‬ ‫8.1‬ ‫55.4‬ ‫7.8‬ ‫0‬ ‫3.2‬ ‫41.1‬ ‫84.5‬ ‫31.1‬ ‫30.1‬ ‫62.2‬ ‫11.5‬ ‫72.3‬ ‫48.5‬ ‫77.5‬ ‫94.5‬ ‫22.3‬ ‫85.2‬ ‫34.3‬ ‫28.4‬ ‫2.01‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫5.03‬ ‫0‬ ‫5.34‬ ‫5.63‬ ‫8.31‬ ‫1.71‬ ‫6.12‬ ‫5.81‬ ‫11‬ ‫2.43‬ ‫3.83‬ ‫1.23‬ ‫61‬ ‫6.12‬ ‫68.6‬ ‫15.8‬ ‫98.7‬ ‫1.01‬ ‫31.8‬ ‫6.41‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫3.2‬ ‫0‬ ‫0‬ ‫10.1‬ ‫71.1‬ ‫622.0‬ ‫0‬ ‫272.0‬ ‫0‬ ‫403.0‬ ‫0‬ ‫981.0‬ ‫0‬ ‫0‬ ‫0‬ ‫431.0‬ ‫8.01‬ ‫6.31‬ ‫4.71‬ ‫9.41‬ ‫5.43‬ ‫5.82‬ ‫7.22‬ ‫2.82‬ ‫3.21‬ ‫3.05‬ ‫2.73‬ ‫9.65‬ ‫8.33‬ ‫6.26‬ ‫2.66‬ ‫42‬ ‫1.38‬ ‫5.03‬ ‫1.23‬ ‫8.84‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫3.2‬ ‫58.6‬ ‫0‬ ‫67.1‬ ‫785.0‬ ‫75.6‬ ‫65.2‬ ‫718.0‬ ‫379.0‬ ‫21.9‬ ‫572.0‬ ‫865.0‬ ‫631.0‬ ‫91.0‬ ‫39.1‬ ‫435.0‬ ‫בעל‬ ‫8.1‬ ‫55.4‬ ‫0‬ ‫50.4‬ ‫3.2‬ ‫75.4‬ ‫9.1‬ ‫657.0‬ ‫53.2‬ ‫40.2‬ ‫51.3‬ ‫53.4‬ ‫92.7‬ ‫43.3‬ ‫73.1‬ ‫56.2‬ ‫34.8‬ ‫4‬ ‫2.6‬ ‫18.4‬ ‫בקר‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫בקׁשה‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫911.0‬ ‫0‬ ‫492.0‬ ‫622.0‬ ‫787.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫728.0‬ ‫0‬ ‫בׂשמ‬ ‫4.41‬ ‫0‬ ‫0‬ ‫53.1‬ ‫6.4‬ ‫75.4‬ ‫42.5‬ ‫71.5‬ ‫69.3‬ ‫58.3‬ ‫65.2‬ ‫44.5‬ ‫98.3‬ ‫2.8‬ ‫93.4‬ ‫61.4‬ ‫45.3‬ ‫83.4‬ ‫71.3‬ ‫7.41‬ ‫0‬ ‫0‬ ‫0‬ ‫7.2‬ ‫3.2‬ ‫0‬ ‫832.0‬ ‫10.1‬ ‫437.0‬ ‫622.0‬ ‫791.0‬ ‫445.0‬ ‫0‬ ‫806.0‬ ‫0‬ ‫757.0‬ ‫0‬ ‫183.0‬ ‫672.0‬ ‫431.0‬ ‫בתולה‬ ‫95.3‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫832.0‬ ‫657.0‬ ‫30.1‬ ‫95.1‬ ‫787.0‬ ‫0‬ ‫0‬ ‫806.0‬ ‫0‬ ‫981.0‬ ‫0‬ ‫0‬ ‫831.0‬ ‫0‬ ‫גבורה‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫445.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫גדולה‬ ‫0‬ ‫0‬ ‫0‬ ‫53.1‬ ‫0‬ ‫0‬ ‫13.1‬ ‫62.1‬ ‫0‬ ‫354.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫גולה‬ ‫8.1‬ ‫6.31‬ ‫7.8‬ ‫0‬ ‫3.2‬ ‫0‬ ‫911.0‬ ‫621.0‬ ‫44.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫219.0‬ ‫41.7‬ ‫0‬ ‫259.0‬ ‫259.0‬ ‫0‬ ‫0‬ ‫גורל‬ ‫‪gather results‬‬ ‫אב‬ ‫אבדנ‬ ‫אבל‬ ‫אחׁשדרפנימ‬ ‫אי‬ ‫אמנה‬ ‫ארבע‬ ‫ארגמנ‬ ‫בינ‬ ‫בירה‬ ‫בית‬ ‫ביתנ‬ ‫בכי‬ ‫בנ‬ ‫בת‬
Next steps Usage by ETCBC workflow for adding annotations Wider Digital Humanities pattern seeking Wido van Peursen, Janet Dyk, whoever needs new kinds of data in and out the database ! Rens Bod ! ! Incorporate in NLPLAB VU Combine with POIO NEO4J backend ? Discuss at workshops Wouter van Atteveldt ! Peter Bouda ! TLA Nijmegen (done) CLIN (accepted) DH2014?
Links Docs: laf-fabric.readthedocs.org Github: github.com/laf-fabric ETCBC: vu.nl/etcbc LAF: iso.org/laf
slideshare.net/dirkroorda thank you

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LAF Fabric

  • 1. LAF Fabric Dirk Roorda DANS/TLA 2013-12-12 VU/ETCBC Amsterdam
  • 2. What is LAF? Linguistic Annotation Framework ISO standard 24612:2012 Nancy Ide, Laurent Romary A data model for stand-off markup plus a serialization advice: GrAF
  • 3. LAF examples OANC Open American National Corpus WIVU-ETCBC Text Database Hebrew Bible
  • 4. OANC 17K 8 feb 2013 ch5-logical.xml 217K 8 feb 2013 ch5-mpqa.xml 265K 8 feb 2013 ch5-nc.xml 16K 8 feb 2013 ch5-ne.xml 1,3M 8 feb 2013 ch5-penn.xml 1,4M 8 feb 2013 ch5-ptb.xml 990K 8 feb 2013 ch5-ptbtok.xml 48K 8 feb 2013 ch5-s.xml 274K 8 feb 2013 ch5-seg.xml 177K 8 feb 2013 ch5-vc.xml 2,6K 3 jun 09:05 ch5.hdr 31K 8 feb 2013 ch5.txt 19K 8 feb 2013 resource-header.xml
  • 5. text Semantics enters with purpose. 10 1-21 210 For this to be true, it is not necessary that the carriers of purpose, say, the same bacterium heading upstream in the glucose gradient, be conscious. I hope my definition of an autonomous agent is useful, an autocatalytic system carrying out a work cycle, now rather broadened by the realization that autonomous agents also do often detect and measure and record displacements of external systems from equilibrium that can be used to extract work, then do extract work, propagating work and constraint construction, from their environment.
  • 6. annotations <region xml:id="mpqa-r64" anchors="2101 2110"/> ! * ... <node xml:id="mpqa-n64"> * <link targets="mpqa-r64"/> * </node> * <a xml:id="mpqa-N81257" label="target" ref="mpqa-n64" as="mpqa"> <fs> <f name="id" value="semantics"/> </fs> </a> !
  • 7. headers Resource header Primary data header Annotation header Metadata, namespaces, annotation labels, statistics
  • 8. SHEBANQ number_within_chapter=1 sentence sentence_atom clause_atom_number=1 clause_atom_relation=0 clause_atom_relation_daughter_tense=unknown clause_atom_relation_kind=No_relation clause_atom_relation_mother_tense=unknown clause_atom_relation_preposition_class=none clause_atom_type=xQtl indentation=0 clause_atom annotations (features) n_88917 clause n_28737 r_11 .. r_1 parents phrase parents phrase_atom n_40770 parents mother nodes n_object id p_7 regions 122r_monad number 123 n_12 r_10 r_11 w_6 106110-121 105 109 subphrase parents word n_11 <region xml:id="w_1" anchors="24 24"/> n_10 lexeme_utf8=‫ראשׁית‬ old_lexeme_utf8=‫ראשׁית‬ vocalized_lexeme_utf8=‫ראשׁית‬ ִ ֵ surface_consonants_utf8=‫ראשׁית‬ graphical_lexeme_utf8=‫ראשׁי‬ ִ֖ ֵ n_77637 pare parents parents nts word <region xml:id="r_2" anchors="6 23"/> <node xml:id="n_3"><link targets="r_2"/></node> <a xml:id="a_3" label="word" ref="n_3" as="monads"/> r_11 .. r_5 pare nts r_11 .. r_9 word <region xml:id="r_1" anchors="0 5"/> <node xml:id="n_2"><link targets="r_1"/></node> <a xml:id="a_2" label="word" ref="n_2" as="monads"/> n_59559 n_77638 primary text UNICODE-utf8 <a xml:id="a_f22" label="ft" ref="n_3" as="utf8"><fs> <f name="lexeme_utf8" value="‫>/"ראׁשית‬ <f name="old_lexeme_utf8" value="‫>/"ראׁשית‬ <f name="vocalized_lexeme_utf8" value="‫>/"ֵראׁשית‬ ִ <f name="surface_consonants_utf8" value="‫>/"ראׁשית‬ <f name="graphical_lexeme_utf8" value="‫>/"ֵראׁשי‬ ִ֖ </fs></a> parents labeled edges subphrase annotations (empty) <node xml:id="n_88917"> <link targets="r_1 r_2 r_3 r_4 r_5 r_6 r_7 r_8 r_9 r_10 r_11"/> </node> <a xml:id="a_88917" label="sentence_atom" ref="n_88917" as="lingo"/> <a xml:id="a_f71355" label="ft" ref="n_88917" as="lingo"><fs> <f name="sentence_atom_number" value="0"/> </fs></a> <edge xml:id="e_1" from="n_88917" to="n_84383"/> <a xml:id="a_e1" label="parents" ref="e_1" as="link"/> n_34680 r_11 .. r_5 link to regions n_84383 r_11 .. r_1 r_11 .. r_1 parents r_11 .. r_1 determination=determined is_apposition=false number_within_clause=4 phrase_function=Objc phrase_type=PP parent Linguistic Annotation Framework word n_9 r_9 r_8 w_5 97- 9392 104 96 r_7 .. r_5 pa parents parents re nts word word word n_8 n_7 r_7 72-91 r_6 6871 word n_6 w_4 r_5 67 59-66 word n_4 n_5 w_3 58 r_4 40-57 word w_2 39 r_3 25-38 word n_3 w_1 24 n_2 r_2 6-23 r_1 0-5 ‫בּראשׁית בּרא א.הים את השּׁמים ְואת הארץ׃‬ ֶ ָֽ ָ ֵ֥ ִ ַ֖ ָ ַ ֵ֥ ִ֑ ֱ ָ֣ ָ ִ֖ ֵ ְ
  • 9. More about SHEBANQ data 2.27 GB XML 99.2% nodes 1,453,175 edges 1,524,637 regions 800,087 features 42,545,492 xml ids 12,831,550 words 426,499
  • 12. Performance Problems POIO: load time +60 min RAM +20 GB ExistDb: load time +30 min (initial) count features +60 min nodes/edges/features directly modeled as objects in Python ! ! xquery not a handy tool for relevant queries need extensive index building identifier chasing
  • 14. What is it? a compiler LAF-XML ==> Python arrays 2,270 MB ==> 485 MB binary data 60 min load time ==> 1 s a task execution environment runs custom Python scripts offering them a LAF API
  • 15. Where is it? clone it from Github https:/ /github.com/dirkroorda/laffabric run it locally share your custom tasks share your own annotations
  • 16. Example: Esther linguistic variation among the bible books count the common nouns of Esther compare their freqs in Esther with those in other books of the Bible
  • 17. Wanted: a tab separated file with frequencies for 216 common nouns for all books Genesis Exodus Lamentations Esther Daniel Ezra ‫אב‬ 27.8 3.31 2.94 2.27 6.95 9.1 ‫אבדנ‬ 0 0 0 1.51 0 0 ‫אבל‬ 0.534 0 1.47 1.51 1.16 0 ‫אגרת‬ 0 0 0 1.51 0 0 ‫אורה‬ 0 0 0 0.755 0 0 ‫אח‬ 23.8 2.34 0 0.755 0 6.82 ‫אחד‬ 6.28 13.6 0 3.78 7.34 6.82 ‫אחר‬ 11.4 3.86 0 1.51 1.93 2.27 ‫אחׁשדרפנימ‬ 0 0 0 2.27 0 0.569 ‫אי‬ 0.134 0 0 0.755 0.386 0 ‫אינ‬ 4.94 3.03 16.2 7.55 3.48 2.27
  • 18. <a xml:id="amf21" label="ft" ref="n3"><fs> <f name="noun_type" value="common"/> <f name="part_of_speech" value="noun"/> <f name="phrase_dependent_part_of_speech" value="noun"/> <f name="pronoun_type" value="none"/> </fs></a> <a xml:id="amf33" label="ft" ref="n4"><fs> <f name="noun_type" value="none"/> <f name="part_of_speech" value="verb"/> <f name="phrase_dependent_part_of_speech" value="verb"/> <f name="pronoun_type" value="none"/> </fs></a> <a xml:id="amf45" label="ft" ref="n5"><fs> <f name="noun_type" value="common"/> <f name="part_of_speech" value="noun"/> <f name="phrase_dependent_part_of_speech" value="noun"/> <f name="pronoun_type" value="none"/> </fs></a> Given(1): LAF annotation files with part of speech features
  • 19. Given (2) Information about the books (where they start and end) <region xml:id="s_1254379" anchors="4609273 4664382"/> <node xml:id="n1254379"><link targets="s_1254379"/></node> <a xml:id="as1254379" label="db" ref="n1254379"><fs> <f name="otype" value="book"/> <f name="oid" value="1254379"/> <f name="monads" value="368500-373120"/> <f name="minmonad" value="368500"/> <f name="maxmonad" value="373120"/> </fs></a> <a xml:id="asf34" label="sft" ref="n1254379"><fs> <f name="book" value="Esther"/> </fs></a>
  • 20. a small python script to the e d workbench! co ce target_book = "Esther" ur for node in NN(): so this_type = F.shebanq_db_otype.v(node) if this_type == "word": p_o_s = F.shebanq_ft_part_of_speech.v(node) if p_o_s == "noun": noun_type = F.shebanq_ft_noun_type.v(node) if noun_type == "common": words[book_name] += 1 lexeme = F.shebanq_ft_lexeme_utf8.v(node) lexemes[book_name][lexeme] += 1 elif this_type == "book": book_name = F.shebanq_sft_book.v(node) books.append(book_name) ontarget = F.shebanq_sft_book.v(node) == target_book
  • 21. Declare features "features": { "shebanq": { "node": [ "db.otype", "ft.part_of_speech,noun_type,lexeme_utf8", "sft.book", ], "edge": [ ], }, The workbench will load selected features unload other features
  • 22. Receive task object def task(graftask): (msg, NN, F, X) = graftask.get_mappings() ! And use supplied methods for rapid data access.
  • 24. ‫‪Nah‬‬ ‫‪Obadiah Jonah‬‬ ‫‪Micah‬‬ ‫‪Joel‬‬ ‫‪Amos‬‬ ‫‪Jeremiah Ezekiel‬‬ ‫‪Hosea‬‬ ‫‪Judges‬‬ ‫‪I_SamuelII_Samuel‬‬ ‫‪I_Kings II_Kings Isaiah‬‬ ‫‪Genesis Exodus‬‬ ‫‪Leviticus Numbers Deuteronomy‬‬ ‫‪Joshua‬‬ ‫95.3‬ ‫0‬ ‫0‬ ‫7.2‬ ‫3.2‬ ‫41.1‬ ‫12.3‬ ‫49.7‬ ‫80.3‬ ‫6.51‬ ‫7.81‬ ‫26.7‬ ‫9.21‬ ‫3.71‬ ‫16.9‬ ‫4.31‬ ‫6.11‬ ‫67.4‬ ‫13.3‬ ‫8.72‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫8.1‬ ‫0‬ ‫0‬ ‫50.4‬ ‫0‬ ‫0‬ ‫911.0‬ ‫873.0‬ ‫492.0‬ ‫0‬ ‫0‬ ‫718.0‬ ‫342.0‬ ‫0‬ ‫0‬ ‫981.0‬ ‫0‬ ‫0‬ ‫0‬ ‫435.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫אגרת‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫אורה‬ ‫95.3‬ ‫0‬ ‫4.71‬ ‫7.2‬ ‫3.2‬ ‫24.3‬ ‫70.1‬ ‫93.2‬ ‫30.1‬ ‫31.1‬ ‫61.2‬ ‫52.9‬ ‫29.2‬ ‫18.8‬ ‫75.3‬ ‫80.9‬ ‫85.2‬ ‫83.4‬ ‫43.2‬ ‫8.32‬ ‫אח‬ ‫0‬ ‫55.4‬ ‫7.8‬ ‫67.6‬ ‫0‬ ‫41.1‬ ‫7.21‬ ‫67.1‬ ‫49.2‬ ‫74.7‬ ‫4.21‬ ‫35.9‬ ‫5.01‬ ‫2.8‬ ‫5.61‬ ‫29.4‬ ‫3.42‬ ‫33.9‬ ‫6.31‬ ‫82.6‬ ‫אחד‬ ‫0‬ ‫0‬ ‫0‬ ‫50.4‬ ‫5.11‬ ‫99.7‬ ‫89.2‬ ‫8.6‬ ‫74.1‬ ‫20.7‬ ‫11‬ ‫1.61‬ ‫9.21‬ ‫8.21‬ ‫11‬ ‫68.5‬ ‫49.3‬ ‫67.4‬ ‫68.3‬ ‫4.11‬ ‫אחר‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫70.1‬ ‫36.0‬ ‫97.2‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫431.0‬ ‫8.01‬ ‫0‬ ‫7.8‬ ‫67.6‬ ‫9.6‬ ‫1.71‬ ‫68.2‬ ‫2.11‬ ‫5.31‬ ‫35.4‬ ‫29.4‬ ‫80.4‬ ‫20.8‬ ‫2.8‬ ‫73.1‬ ‫86.5‬ ‫85.2‬ ‫4‬ ‫30.3‬ ‫49.4‬ ‫אינ‬ ‫4.41‬ ‫9.04‬ ‫1.62‬ ‫50.4‬ ‫2.9‬ ‫4.11‬ ‫6.01‬ ‫3.02‬ ‫4.9‬ ‫8.82‬ ‫7.61‬ ‫4.83‬ ‫3.15‬ ‫5.06‬ ‫8.91‬ ‫71‬ ‫8.71‬ ‫9.71‬ ‫2.31‬ ‫2.12‬ ‫איׁש‬ ‫95.3‬ ‫0‬ ‫0‬ ‫53.1‬ ‫0‬ ‫0‬ ‫67.4‬ ‫873.0‬ ‫30.1‬ ‫94.2‬ ‫31.4‬ ‫71.5‬ ‫18.6‬ ‫6.01‬ ‫92.3‬ ‫98.1‬ ‫1.41‬ ‫0‬ ‫25.1‬ ‫762.0‬ ‫אלפ‬ ‫8.1‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫75.4‬ ‫91.1‬ ‫31.1‬ ‫437.0‬ ‫89.4‬ ‫51.3‬ ‫718.0‬ ‫379.0‬ ‫80.6‬ ‫428.0‬ ‫64.2‬ ‫272.0‬ ‫68.2‬ ‫569.0‬ ‫74.3‬ ‫אמ‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫5.01‬ ‫405.0‬ ‫741.0‬ ‫976.0‬ ‫44.9‬ ‫36.1‬ ‫76.2‬ ‫806.0‬ ‫572.0‬ ‫80.2‬ ‫259.0‬ ‫175.0‬ ‫73.9‬ ‫47.1‬ ‫אמה‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫622.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫8.1‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫41.1‬ ‫832.0‬ ‫93.1‬ ‫67.1‬ ‫354.0‬ ‫389.0‬ ‫718.0‬ ‫342.0‬ ‫219.0‬ ‫428.0‬ ‫865.0‬ ‫0‬ ‫0‬ ‫672.0‬ ‫208.0‬ ‫אמת‬ ‫0‬ ‫55.4‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫832.0‬ ‫621.0‬ ‫44.0‬ ‫0‬ ‫393.0‬ ‫272.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫272.0‬ ‫0‬ ‫0‬ ‫431.0‬ ‫אפר‬ ‫0‬ ‫55.4‬ ‫0‬ ‫5.31‬ ‫0‬ ‫0‬ ‫19.6‬ ‫288.0‬ ‫44.0‬ ‫18.1‬ ‫92.6‬ ‫19.1‬ ‫59.1‬ ‫52.4‬ ‫76.4‬ ‫14.3‬ ‫57.7‬ ‫33.1‬ ‫84.6‬ ‫10.4‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫832.0‬ ‫621.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫403.0‬ ‫0‬ ‫0‬ ‫631.0‬ ‫0‬ ‫85.3‬ ‫0‬ ‫9.62‬ ‫90.9‬ ‫7.8‬ ‫1.13‬ ‫6.72‬ ‫8.22‬ ‫6.32‬ ‫1.43‬ ‫9.72‬ ‫1.61‬ ‫11‬ ‫9.01‬ ‫6.21‬ ‫2.81‬ ‫4.92‬ ‫3.73‬ ‫7.61‬ ‫6.51‬ ‫7.81‬ ‫5.14‬ ‫ארצ‬ ‫8.1‬ ‫0‬ ‫0‬ ‫7.2‬ ‫0‬ ‫17.5‬ ‫26.2‬ ‫45.4‬ ‫67.1‬ ‫3.4‬ ‫74.7‬ ‫3.31‬ ‫4.31‬ ‫12‬ ‫20.3‬ ‫59.7‬ ‫57.7‬ ‫7.41‬ ‫97.5‬ ‫3.02‬ ‫אׁשה‬ ‫0‬ ‫0‬ ‫0‬ ‫53.1‬ ‫3.2‬ ‫0‬ ‫76.1‬ ‫36.0‬ ‫60.2‬ ‫35.4‬ ‫787.0‬ ‫81.2‬ ‫379.0‬ ‫25.1‬ ‫0‬ ‫981.0‬ ‫27.2‬ ‫5.01‬ ‫71.3‬ ‫78.1‬ ‫בגד‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫41.1‬ ‫91.1‬ ‫252.0‬ ‫16.1‬ ‫31.1‬ ‫43.3‬ ‫54.2‬ ‫22.1‬ ‫37.2‬ ‫428.0‬ ‫7.1‬ ‫99.2‬ ‫84.2‬ ‫97.5‬ ‫78.1‬ ‫בד‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫בהט‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫911.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫בוצ‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫בזה‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫בזיונ‬ ‫8.1‬ ‫55.4‬ ‫7.8‬ ‫0‬ ‫3.2‬ ‫41.1‬ ‫84.5‬ ‫31.1‬ ‫30.1‬ ‫62.2‬ ‫11.5‬ ‫72.3‬ ‫48.5‬ ‫77.5‬ ‫94.5‬ ‫22.3‬ ‫85.2‬ ‫34.3‬ ‫28.4‬ ‫2.01‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫5.03‬ ‫0‬ ‫5.34‬ ‫5.63‬ ‫8.31‬ ‫1.71‬ ‫6.12‬ ‫5.81‬ ‫11‬ ‫2.43‬ ‫3.83‬ ‫1.23‬ ‫61‬ ‫6.12‬ ‫68.6‬ ‫15.8‬ ‫98.7‬ ‫1.01‬ ‫31.8‬ ‫6.41‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫3.2‬ ‫0‬ ‫0‬ ‫10.1‬ ‫71.1‬ ‫622.0‬ ‫0‬ ‫272.0‬ ‫0‬ ‫403.0‬ ‫0‬ ‫981.0‬ ‫0‬ ‫0‬ ‫0‬ ‫431.0‬ ‫8.01‬ ‫6.31‬ ‫4.71‬ ‫9.41‬ ‫5.43‬ ‫5.82‬ ‫7.22‬ ‫2.82‬ ‫3.21‬ ‫3.05‬ ‫2.73‬ ‫9.65‬ ‫8.33‬ ‫6.26‬ ‫2.66‬ ‫42‬ ‫1.38‬ ‫5.03‬ ‫1.23‬ ‫8.84‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫3.2‬ ‫58.6‬ ‫0‬ ‫67.1‬ ‫785.0‬ ‫75.6‬ ‫65.2‬ ‫718.0‬ ‫379.0‬ ‫21.9‬ ‫572.0‬ ‫865.0‬ ‫631.0‬ ‫91.0‬ ‫39.1‬ ‫435.0‬ ‫בעל‬ ‫8.1‬ ‫55.4‬ ‫0‬ ‫50.4‬ ‫3.2‬ ‫75.4‬ ‫9.1‬ ‫657.0‬ ‫53.2‬ ‫40.2‬ ‫51.3‬ ‫53.4‬ ‫92.7‬ ‫43.3‬ ‫73.1‬ ‫56.2‬ ‫34.8‬ ‫4‬ ‫2.6‬ ‫18.4‬ ‫בקר‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫בקׁשה‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫911.0‬ ‫0‬ ‫492.0‬ ‫622.0‬ ‫787.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫728.0‬ ‫0‬ ‫בׂשמ‬ ‫4.41‬ ‫0‬ ‫0‬ ‫53.1‬ ‫6.4‬ ‫75.4‬ ‫42.5‬ ‫71.5‬ ‫69.3‬ ‫58.3‬ ‫65.2‬ ‫44.5‬ ‫98.3‬ ‫2.8‬ ‫93.4‬ ‫61.4‬ ‫45.3‬ ‫83.4‬ ‫71.3‬ ‫7.41‬ ‫0‬ ‫0‬ ‫0‬ ‫7.2‬ ‫3.2‬ ‫0‬ ‫832.0‬ ‫10.1‬ ‫437.0‬ ‫622.0‬ ‫791.0‬ ‫445.0‬ ‫0‬ ‫806.0‬ ‫0‬ ‫757.0‬ ‫0‬ ‫183.0‬ ‫672.0‬ ‫431.0‬ ‫בתולה‬ ‫95.3‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫832.0‬ ‫657.0‬ ‫30.1‬ ‫95.1‬ ‫787.0‬ ‫0‬ ‫0‬ ‫806.0‬ ‫0‬ ‫981.0‬ ‫0‬ ‫0‬ ‫831.0‬ ‫0‬ ‫גבורה‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫445.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫גדולה‬ ‫0‬ ‫0‬ ‫0‬ ‫53.1‬ ‫0‬ ‫0‬ ‫13.1‬ ‫62.1‬ ‫0‬ ‫354.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫גולה‬ ‫8.1‬ ‫6.31‬ ‫7.8‬ ‫0‬ ‫3.2‬ ‫0‬ ‫911.0‬ ‫621.0‬ ‫44.0‬ ‫0‬ ‫0‬ ‫0‬ ‫0‬ ‫219.0‬ ‫41.7‬ ‫0‬ ‫259.0‬ ‫259.0‬ ‫0‬ ‫0‬ ‫גורל‬ ‫‪gather results‬‬ ‫אב‬ ‫אבדנ‬ ‫אבל‬ ‫אחׁשדרפנימ‬ ‫אי‬ ‫אמנה‬ ‫ארבע‬ ‫ארגמנ‬ ‫בינ‬ ‫בירה‬ ‫בית‬ ‫ביתנ‬ ‫בכי‬ ‫בנ‬ ‫בת‬
  • 25. Next steps Usage by ETCBC workflow for adding annotations Wider Digital Humanities pattern seeking Wido van Peursen, Janet Dyk, whoever needs new kinds of data in and out the database ! Rens Bod ! ! Incorporate in NLPLAB VU Combine with POIO NEO4J backend ? Discuss at workshops Wouter van Atteveldt ! Peter Bouda ! TLA Nijmegen (done) CLIN (accepted) DH2014?