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 instances This 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 Ord 1 dog terrier cat mouse barking bite yelp -3 1 - 2
Chris Biemann

6. Constructed Example II
Ord 2
dog
terrier
cat
mouse
barking
bite
yelp x -
Ord 3
dog
terrier
cat
mouse
barking
bite
yelp - 1
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. Examples of Iterated Co-occurrences
Order
Reference word
TOP-10 collocations N2
wine
wines, champagne, beer, water, tea, coffee, Wine, alcoholic, beers, cider
S10
wines, grape, sauvignon, chardonnay, noir, pinot, cabernet, spicy, bottle, grapes S1
ringing
phone, bells, phones, hook, bell, endorsement, distinctive, ears, alarm, telephone S2
rung, Centrex, rang, phone, sounded, bell, ring, FaxxMaster, sound, tolled S4
sounded, rung, rang, tolled, tolling, sound, tone, toll, ring, doorbell
pressing
Ctrl, Shift, press, keypad, keys, key, keyboard, you, cursor, menu, PgDn, keyboards, numeric, Alt, Caps, CapsLock, NUMLOCK, NumLock, Scroll
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-onyms synonyms, 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