Unsupervised Morphological Segmentation Based …morpho.aalto.fi/events/morphochallenge2005/bernhard.pdfUnsupervised Morphological Segmentation Based on Word Segments Predictability

Unsupervised Morphological SegmentationBased on

Word Segments Predictability and Alignment

Delphine Bernhard

Unsupervised Segmentation of Words into MorphemesPascal Challenge Workshop, April 12, 2006

Delphine Bernhard (TIMC-IMAG) MorphoChallenge Workshop 1 / 21

Part I

Motivation


Why ?

ContextWork on the morphology of domain-specific vocabulary, esp. medicallanguage (many neoclassical compounds)

Examplesdermatofibrosarcomaglomeroporphyritic

slammograms

(refers to mammograms)

pneumonoultramicroscopicsilicovolcanoconiosis

"a lung disease caused by the inhalation of very fine silica dust,mostly found in volcanoes" = pneumoconiosis(But this is a hoax !)


Why ?


Examplesdermatofibrosarcomaglomeroporphyriticslammograms

(refers to mammograms)pneumonoultramicroscopicsilicovolcanoconiosis



Why ?


Examplesdermatofibrosarcomaglomeroporphyriticslammograms (refers to mammograms)

pneumonoultramicroscopicsilicovolcanoconiosis



Why ?


Examplesdermatofibrosarcomaglomeroporphyriticslammograms (refers to mammograms)pneumonoultramicroscopicsilicovolcanoconiosis



Why ?


Examplesdermatofibrosarcomaglomeroporphyriticslammograms (refers to mammograms)pneumonoultramicroscopicsilicovolcanoconiosis"a lung disease caused by the inhalation of very fine silica dust,mostly found in volcanoes" = pneumoconiosis

(But this is a hoax !)


Why ?


Examplesdermatofibrosarcomaglomeroporphyriticslammograms (refers to mammograms)pneumonoultramicroscopicsilicovolcanoconiosis"a lung disease caused by the inhalation of very fine silica dust,mostly found in volcanoes" = pneumoconiosis(But this is a hoax !)


Objectives

Automatic acquisition of semantic relationships thanks tomorphological relatedness


Part II

Method


Constraints

Take into account all of the following word formation processes:inflectionderivationcompounding

Method not limited to French or English.Distinguish between different types of word segments:

prefixsuffixstemlinking element


Overview of the method

InputList of words

Stages1 Acquisition of prefixes and suffixes2 Acquisition of stems3 Alignment of word segments4 Selection of the best segmentation for each word


Acquisition of prefixes and suffixes [1]

InputLongestwords

Locate positions withlow segment predictability

OutputSegments



InputLongestwords


OutputSegments



InputLongestwords


OutputSegments



Identification of a stem among the segments

Prefixes and suffixes


Extraction of stems

Subtract prefixes and suffixes from all words


Alignment of word segments [1]


Alignment of word segments [2]

Validation of new prefixes and suffixes

Words Known suffixes Potential stems New suffixesA1 A2 A3

hormonal -alhormonotherapy -otherapyhormone -ehormones -es

|A1|+ |A2||A1|+ |A2|+ |A3|

≥ a and|A1|

|A1|+ |A2|≥ b


Selection of the best segmentation


Segmentation of new words

For each word, select segments so that the total cost is minimalCost functions used:

cost1(si) = −logf (si)∑i f (si)

cost2(si) = −logf (si)

maxi[f (si)]


Part III

Results and conclusion


Evaluation

Position of boundariesMorphoChallenge evaluation

Conflation setsCheck if word forms containing the same stem are related

Test on an English corpus on breast cancer(about 86,000 word types).Manually built morphological families for the top 5,000 key wordsResults: F-measure ∼ 50%(Recall = 40% ± 7, Precision = 66% ± 7)


Examples [1]

Words Segmentationschondrosarcomas chondro + sarcoma + scystosarcoma cyst + o + sarcomadermatofibrosarcomas derm + at + o + fibro + sarcoma + sfibroxanthosarcoma fibroxanthosarcomaleiomyosarcoma leiomyo + s + arc + omaleiomyosarcomas leiomyo + sarcoma + sliposarcoma lipo + sarcomalymphangiosarcomas lymph + angiosarcoma + smyxofibrosarcoma myxo + fibro + sarcomamyxosarcomas myxo + sarcoma + sneurofibrosarcoma neur + o + fibro + sarcomaosteosarcoma osteo + sarcomaosteosarcomatous osteosarcoma + toussarcoma sarcomasarcomatoid sarcoma + t + oid


Examples [2]

Words Segmentationsauto-transplant auto + - + transplantauto-transplantation auto + - + transplant + ationautotransplantation auto + transplant + ationpost-transplantation post + - + transplant + ationposttransplantation post + transplant + ationretransplantation re + transplant + ationtransplantability transplantabilitytransplant trans + planttransplanted trans + plant + e + dtransplanting trans + plant + ingtransplants trans + plant + sxenotransplantation xenotransplant + ationxenotransplanted xenotransplant + edxenotransplants xeno + transplants


Main issues

Over-segmentationleiomyo + s + arc + omag + lobul + e

⇒ Low precision

Under-segmentationtransplantabilityxenotransplant + ationxenotransplant + ed

⇒ Low recall


Conclusion

SummaryMethod usable for languages other than French and EnglishPerforms segmentation + distinguishes between different kinds ofsegments

Future workUse other data structures to deal with very, very large corporaDeal with variations within stems (accents, alternations)Evaluate how well word segments predict semantic relationshipsbetween terms


Thank you

Further information:[email protected]

http://www-timc.imag.fr/Delphine.Bernhard/


Documents

Unsupervised Morphological Segmentation Based …morpho.aalto.fi/events/morphochallenge2005/bernhard.pdfUnsupervised Morphological Segmentation Based on Word Segments Predictability