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Morphosemantic Relations in and Across Wordnets: A Study Based on Turkish. Orhan Bilgin, Özlem Çetinoglu, Kemal Oflazer Sabanci University Human Languages and Speech Technologies Laboratory Istanbul, Turkey {orhanb,ozlemc,oflazer@sabanciuniv.edu}. OBJECTIVES.
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Morphosemantic Relations in and Across Wordnets: A Study Based on Turkish Orhan Bilgin, Özlem Çetinoglu, Kemal Oflazer Sabanci University Human Languages and Speech Technologies Laboratory Istanbul, Turkey {orhanb,ozlemc,oflazer@sabanciuniv.edu}
OBJECTIVES • Using morphological processes in Language A, we can: • extract explicit semantic relations in Language A and use these to enrich Wordnet A; • automatically prepare machine-tractable synset glosses for Wordnet A and/or B; and most importantly • discover implicit semantic relations in Wordnet B and use these to enrich Wordnet B.
METHODOLOGY • 1) Determine derivational affixes in Language A • Rule of Thumb: • Prefer productive affixes with predictable semantics
METHODOLOGY The chaotic “agentive” suffix -ci -ci
METHODOLOGY A more well-behaved suffix: - li -li
METHODOLOGY 2) Define the semantic effects of the affixes
METHODOLOGY 2) Extract morphosemantically-related pairs boulder builder deer dresser father founder her killer laser maker mother never teacher boulder builder deer dresser father founder her killer laser maker mother never teacher MORPHOLOGICAL ANALYZER CANDIDATES WINNERS ROOT (v) + AGENT ?
METHODOLOGY 2) Extract morphosemantically-related pairs build - builder dress - dresser found - founder kill - killer make - maker teach - teacher
METHODOLOGY 3) Link pair members to ILI records dress (v) dresser (n) ? • put on clothes • dress in a certain manner • dress with elaborate care • put a dressing on • convert into leather • apply a bandage to • give a neat appearance to • arrange hair attractively • put a finish on • kill and prepare for consumption • arrange in ranks • provide with clothes • cut back the growth of • furniture for keeping clothes • person who dresses in a particular way • a wardrobe assistant for an actor • a cabinet with shelves • low table with mirror or mirrors (1709-1784)
USES 1) Extract explicit semantic relations in the language taş polimer iyon kemik billur kireç plastik izomer keratin taşlaşmak polimerleşmek iyonlaşmak kemikleşmek billurlaşmak kireçleşmek plastikleşmek izomerleşmek keratinleşmek taşlaştırmak polimerleştirmek iyonlaştırmak kemikleştirmek billurlaştırmak kireçleştirmek plastikleştirmek izomerleştirmek keratinleştirmek IS_CAUSED_BY BECOME
USES 2) Share relations with other wordnets a) Pairs in importing language are morphologically related STATE_OF deli delilik EXPORTING LANGUAGE INTERLINGUAL INDEX 02005975-a 13580347-n mad madness IMPORTING LANGUAGE
USES 2) Share relations with other wordnets a) Pairs in importing language are morphologically related STATE_OF deli delilik EXPORTING LANGUAGE INTERLINGUAL INDEX 02005975-a 13580347-n mad madness IMPORTING LANGUAGE STATE_OF
USES 2) Share relations with other wordnets b) Pairs in importing language are morphologically unrelated CAUSES yıkmak yıkılmak EXPORTING LANGUAGE INTERLINGUAL INDEX 01931110-v 01614562-v tear down collapse IMPORTING LANGUAGE
USES 2) Share relations with other wordnets b) Pairs in importing language are morphologically unrelated CAUSES yıkmak yıkılmak EXPORTING LANGUAGE INTERLINGUAL INDEX 01931110-v 01614562-v tear down collapse IMPORTING LANGUAGE CAUSES
USES 3) Prepare simple synset glosses WITH omurga omurgalı EXPORTING LANGUAGE INTERLINGUAL INDEX 02422440-a 05268544-n spine vertebrate IMPORTING LANGUAGE
USES 3) Prepare simple synset glosses WITH omurga omurgalı EXPORTING LANGUAGE INTERLINGUAL INDEX 02422440-a 05268544-n spine vertebrate IMPORTING LANGUAGE vertebrate == with spine
USES 3) Prepare simple synset glosses Some examples based on Turkish Wordnet:
RESULTS The current wordlist of Turkish contains a substantial number of words derived from a small set of suffixes.
RESULTS • Detailed analysis of two suffixes: • Although Turkish Wordnet is a small-sized resource (~10,000 synsets), it contains a significant number of synsets involving these two suffixes. • In only a few cases does PWN indicate a CAUSES relation between the respective English synsets. • In the case of the BECOME pairs, PWN provides the underspecified relation “ENG_DERIVATIVE”.