1. Identification of Composite Named Entities in a Spanish Textual Database Sofía N. Galicia-Haro Facultad de Ciencias - UNAM Alexander F. Gelbukh and Igor A. Bolshakov Lab. Lenguaje Natural CIC – IPN México, D. F.

2. Contents Introduction Named Entities in Textual Databases NE Analysis Recognition Method Conclusions

3. Contents Introduction Named Entities in Textual Databases NE Analysis Recognition Method Conclusions

4. Textual Databases They have been entered to computers and to Web to save tons of paper to allow people to have remote access to provide much better access to texts in electronic format, etc. Searching through this huge material for information is a time consuming task

5. Named Entities NE mentioned in textual databases constitute an important part of their semantic contents A collection of political electronic texts shows that almost 50% of the total sentences contains at least one NE This indicates the relevance of NE identification and its role in document indexing and retrieval

6. Composite Named Entities NE with coordinated constituents Luz y Fuerza del Centro NE with prepositional phrases Ejército Zapatista de Liberación Nacional

7. Contents Introduction Named Entities in Textual Databases NE Analysis Recognition Method Conclusions

8. NEs in Mexican Textual DB NEs appear at least in 50% of the sentences Selection of Collection 1 taken for training Collections of Political Mexican texts Coll. 1Coll. 2 # Sentences442,719208,298 # Sentences w/named entities243,165100,602

9. Initial NE Recognition Step Identification of linguistic characteristics Example: Prepositions link two different NE are included in the NE Identification of style characteristics Ex: Specific words introduce convention names coordinadora del programa Mundo Maya ‘Mundo Maya program’s coordinator’

10. Contents Introduction Named Entities in Textual Databases NE Analysis Recognition Method Conclusions

11. Training File A Perl program extracts “compounds” Los miembros del Ejercito Federal (1) lejos de aplicar la Ley sobre Armas de Fuego y Explosivos parecen (2) proteger a los participantes en el tiroteo. Compounds contain no more than three non- capitalized words between capitalized words Compounds are left- and right- delimited by a punctuation marks or a word

12. Sentences of coll.1 From 243,165 sentences 472,087 compounds 500 randomly selected sentences were manually analyzed Main result from analysis: Syntactic ambiguity is frequent

13. Syntactic Ambiguity Coordination of coordinated names Comisión Federal de Electricidad y Luz y Fuerza del Centro Margarita Diéguez y Armas y Carlos Virgilio Prepositional phrase attachment Different names linked by prepositions Comandancia General del Ejército Zapatista de Liberación Nacional

14. Contents Introduction Named Entities in Textual Databases NE Analysis Recognition Method Conclusions

15. Knowledge Contributions External lists Linguistic knowledge Heuristics Statistics

16. External Lists Hand-made list of similes (625 items) paz y justicia ‘peace and justice’ Latinoamérica y el Caribe Hand-made list of words Lists from the WEB personal names (697 items) main Mexican cities (910 items)

17. Linguistic Knowledge Examples of linguistic restrictions Lists of groups of capitalized words Corea del Sur (1), Taiwan (2), Checoslovaquia (3) y Sudáfrica (4) Preposition por followed by indefinite article cannot be the link within a personal name Cuauhtémoc Cárdenas (1) por la Alianza por la Ciudad de México (2)

18. Heuristics and Statistics Heuristic example: a first name can be the part of only one name sequence among those coordinated Ex.: Margarita Diéguez y Armas y Carlos Virgilio Carlos belongs to the list of first names. Thus there are two name sequences here: Margarita Diéguez y Armas Vs. Carlos Virgilio Statistics from training file With a high score, Estados Unidos is a 2-word group Thus Estados Unidos sobre México could be separated

19. Application of the Method Obtaining compounds with functional words Using previous resources, the program decides on splitting, delimiting or leaving each compound as such Extract coordinated groups prepositional phrases the rest of groups of capitalized words

20. Results - 1 Obtained from 500 sentences of Coll. 2 Number of: Coordinated Groups Prepositional Phrase Groups total Precision546989 Recall486787

21. Results - 2 Total: 1496 NE 63 names with coordination 167 prepositional groups To compare with: Carreras, X., L. Márques and L. Padró. Named Entity Extraction using AdaBoost, CoNLL-2002 92% for precision and 91% for recall However, the test file only includes one coordinated name If a NE is embedded in another one, only the top level entity was marked

22. Conclusions We present a method to identify and disambiguate groups of capitalized words Our work is focused on composite named entities Our method use extremely short lists and a small POS-marked dictionary The method use heterogeneous knowledge to decide on splitting or joining groups with capitalized words

23. sofia@fciencias.unam.mx gelbukh@cic.ipn.mx igor@cic.ipn.mx Thanks!