Automatic Acquisition of Paradigmatic Relations using Iterated Co-occurrences

1. Automatic Acquisition of Paradigmatic Relations using Iterated Co-occurrences Chris Biemann, Stefan Bordag, Uwe Quasthoff University of Leipzig, NLP Department LREC 2004, Learning & Acquisition (II), 27th of May 2004

2. Sets of Words • Our goal is the automatic extension of homogenous word sets, i.e. WordNet synsets or small subtrees of some hierarchy • We collect methods and apply them, eventually in combination • Mind experiment: the computer as „associator“:Input: some example concepts- Detection of the relation- Output of additional instancesThis can be done semi-supervised • Necessary:- very large text corpus- features- methods Chris Biemann

3. Statistical Co-occurrences • occurrence of two or more words within a well-defined unit of information (sentence, nearest neighbors) • Significant Co-occurrences reflect relations between words • Significance Measure (log-likelihood):- k is the number of sentences containing a and b together- ab is (number of sentences with a)*(number of sentences with b)- n is total number of sentences in corpus Chris Biemann

4. Iterating Co-occurrences • (sentence-based) co-ocurrences of first order:words that co-occur significantly often together in sentences • co-occurrences of second order: words that co-occur significantly often in collocation sets of first order • co-occurrences of n-th order:words that co-occur significantly often in collocation sets of (n-1)th order When calculating a higher order, the significance values of the preceding order are not relevant. A co-occurrence set consists of the N highest ranked co-occurrences of a word. Chris Biemann

5. Constructed Example I Chris Biemann

6. Constructed Example II Chris Biemann

7. Properties of Iterated Co-occurrences • after some iterations the sets remain more or less stable • the sets are somewhat semantically homogeneous • sometimes, they have to do nothing with the reference word • calculations performed until 10th order. • Example for TOP 20 NB-collocations of 10th order for „erklärte“ [explained]: sagte, schwärmte, lobt, schimpfte, meinte, jubelte, lobte, resümierte, schwärmt, Reinhard Heß, ärgerte, kommentierte, urteilte, analysierte, bilanzierte, freute, freute sich, Bundestrainer, freut ,gefreut[said, enthused, praises, grumbled, meant, was jubilant, praised, summarized, dreamt, Reinhard Hess, annoyed, commentated, judged, analyzed, balanced, made happy, was pleased, coach of the national team, is pleased, been pleased] Chris Biemann

8. Mapping co-occurrences to graphs • For all words having co-occurrences, form nodes in a graph. • Connect them all by edges, initialize edge weight with 0 • For every co-occurrence of two words in a sentence, increase edge weight by significance Chris Biemann

9. First Iteration Step • The two black nodes A and B get connected in the step if there are many nodes C which are connected to both A and B • The more Cs, the higher the weight of the new edge existing connection new connection Chris Biemann

10. Second Iteration Step • The two black nodes A and B get connected in the step if there are many (dark grey) nodes Ds which are connected to both A and B. • The connections between the nodes Ds and the nodes A and B were constructed because of (light gray) nodes Es and Fs, respectively Es Ds Fs former connection existing connection new connection B A Chris Biemann

11. Collapsing bridging nodes • Upper bound for path length in iteration n is 2n. • However, some of the bridging nodes collapse, giving rise to self-keeping clusters of arbitrary path length, which are invariant under iteration. Upper 5 nodes: invariant cluster A, B are being absorbed by this cluster Chris Biemann

12. Order Reference word TOP-10 collocations N2 wine wines, champagne, beer, water, tea, coffee, Wine, alcoholic, beers, cider S10 wine wines, grape, sauvignon, chardonnay, noir, pinot, cabernet, spicy, bottle, grapes S1 ringing phone, bells, phones, hook, bell, endorsement, distinctive, ears, alarm, telephone S2 ringing rung, Centrex, rang, phone, sounded, bell, ring, FaxxMaster, sound, tolled S4 ringing sounded, rung, rang, tolled, tolling, sound, tone, toll, ring, doorbell S10 pressing Ctrl, Shift, press, keypad, keys, key, keyboard, you, cursor, menu, PgDn, keyboards, numeric, Alt, Caps, CapsLock, NUMLOCK, NumLock, Scroll Examples of Iterated Co-occurrences Chris Biemann

13. Intersection of Co-occurrence Sets: resolving ambiguity Herz-Bube Becker bedient - folgenden - gereizt - Karo-Buben - Karo-Dame - Karo-König - Karte - Karten - Kreuz-Ass - Kreuz-Dame - Kreuz-Hand - Kreuz-König - legt - Mittelhand - Null ouvert - Pik - Pik-Ass - Pik-Dame - schmiert - Skat - spielt - Spielverlauf - sticht - übernimmt - zieht - Agassi - Australian Open - Bindewald - Boris - Break - Chang - Dickhaut - - gewann - Ivanisevic - Kafelnikow - Kiefer - Komljenovic - Leimen - Matchball - Michael Stich - Monte Carlo - Prinosil - Sieg - Spiel - spielen - Steeb - Teamchef Stich Achtelfinale - Aufschlag - Boris Becker - Daviscup - Doppel - DTB –Edberg - Finale - Graf - Haas - Halbfinale - Match - Pilic - Runde - Sampras - Satz - Tennis - Turnier - Viertelfinale - Weltrangliste - Wimbledon Alleinspieler - Herz - Herz-Dame - Herz-König - Hinterhand - Karo - Karo-As - Karo-Bube - Kreuz-As - Kreuz-Bube - Pik-As - Pik-Bube - Pik-König - Vorhand - Becker - Courier - Einzel - Elmshorn - French Open - Herz-As - ins - Kafelnikow - Karbacher - Krajicek - Kreuz-As - Kreuz-Bube - Michael Stich - Mittelhand - Pik-As - Pik-Bube - Pik-König Stich Chris Biemann

14. Example: NB-collocations of 2nd order warm, kühl, kalt • Disjunction and filtering for adjectives of collocation sets for warm, kühl, kalt [warm, cool, cold] results in:abgekühlt, aufgeheizt, eingefroren, erhitzt, erwärmt, gebrannt, gelagert, heiß, heruntergekühlt, verbrannt, wärmer[cooled down, heated, frozen, heated up, warms up, burned, stored, hot, down-cooled, burned, more warmly] • emotional reading „abweisend“ [repelling] for kühl, kalt is eliminated Chris Biemann

15. Detection of X-onymssynonyms, antonyms, (co)-hyponyms... • Idea: Intersection of co-occurrence sets of two X-onyms as reference words should contain X-onyms • lexical ambiguity of one reference word does not deteriorate the result set • Method:- Detect word class for reference words- calculate co-occurrences for reference words- filter co-occurrences w.r.t the word class of the reference words (by means of POS tags)- perform disjunction of the co-occurrence sets- output result • ranking can be realized over significance values of the co-occurrences Chris Biemann

16. Mini-Evaluation • Experiments for different data sources, NB-collocations of 2nd and 3rd order • fraction of X-onyms in TOP 5 higher than in TOP 10  ranking method makes sense • disjunction of 2nd-order and 3rd-order collocations almost always empty  different orders exhibit different relations • satisfactory quantity, more through larger corpora • quality: for unsupervised extension not precise enough Chris Biemann

17. Word Sets for Thesaurus Expansion Application: thesaurus expansion start set: [warm, kalt] [warm, cold]result set: [heiß, wärmer, kälter, erwärmt, gut, heißer, hoch, höher, niedriger, schlecht, frei] [hot, warmer, colder, warmed, good, hotter, high, higher, lower, bad, free] start set: [gelb, rot] [yellow, red]result set: [blau, grün, schwarz, grau, bunt, leuchtend, rötlich, braun, dunkel, rotbraun, weiß] [blue, green, black, grey, colorful, bright, reddish, brown, dark, red-brown, white] start set: [Mörder, Killer] [murderer, killer]result set: [Täter, Straftäter, Verbrecher, Kriegsverbrecher, Räuber, Terroristen, Mann, Mitglieder, Männer, Attentäter] [offender, delinquent, criminal, war criminal, robber, terrorists, man, members, men, assassin Chris Biemann

18. More Examples in English Intersection of N2-Order collocation sets Chris Biemann

19. Questions? THANK YOU ! Chris Biemann