Coping with OCR errors in NE search

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1 Coping with OCR errors in NE search Heikki Keskustalo School of Information Sciences University of Tampere Named Entity Recognition in Digital Humanities Workshop University of Helsinki June

2 Contents 1. Noisy Historical Finnish Test Collection Approach and NE Example Problematic Variant Candidates Approaches to Cope 2. Query Expansion Study A. Järvelin & al. (2015) IR from noisy historical Finnish collections

3 Test Collection approach 1. Documents (180,000) Historical Newspaper articles from National Library of Finland Years , digitized 2. Topics (56) #44 Weather forecasting 3. Graded relevance judgments Topic Highly (3) Fairly (2) Marginal (1) Non- rel (0) Total #

4 Suez Canal (NE) Diacony institution (NE) Topics Ida Aalberg s career abroad (NE) The murder of Alexandra Gedda (NE) Plato (NE) Imatra rapid (NE) Ending of slavery in Brazil (NE) Minna Canth s play Worker s wife (NE) Weather forecasting (Topical) +47 Topics

5 Suez Canal 1. TITLE [Suez Canal] 2. DESCRIPTION Defining Topic [Suez Canal was designed and built between 1854 and 1869 by the International Canal Company led by Ferdinand de Lesseps, in order to connect the Mediterranean Sea and the Red Sea in a route that even large ships could traverse. The great undertaking was completed, but faced financial problems from the start, and became a bone of contention in international politics for decades.] 3. NARRATIVE [In order to be relevant, the article needs to report some real news related specifically to the Suez Canal. It is not relevant just to mention that a ship has passed through the Canal, or to mention Suez Canal as a place name.]

6 Defining relevance levels Guidelines for assessors: 0 (non-relevant) = mentioning a ship passing through the Canal; cholera news; Suez Canal only mentioned as a place name Examples: # (clear case); # (news correction related to opening of the Canal) 1 (marginally relevant) = a brief news story (describing the Canal, or its political significance) Examples: # (clear); #34032 (Panama C at the focus; does contain detailed information about SC construction costs) 2 (fairly relevant) = longer news story about the Canal Examples: #42783 and.. 3 (highly relevant) = long news story about the Canal Examples: # and..

7 Using Test Collection 1. Retrieve documents 2. using methods compared A (e.g., short Q) B (e.g., QE) 3. Inspect document lists returned 4. Compare A and B select effectiveness measure e.g., MAP,

8 1. The image: Problems with NE Suez Canal? 2. Human interpretahon:. [espanjalaiset, joille Suezin kanavan puo- on tärkeä Philippini saarien wuoksi Itä- InHassa, epäluuloisin silmin katselewat Englannin toimia EgypHssä.] 3. OCR (Computer): espanialaiset, joille Suetzin lanawan puolueeltomui.s on tärkeä Philippini saarien wuolsi Itä-Indiassa, epäluuloisin silmin katselemat Englannin toimia Egyptissä - Digital clipping - Uusi Suometar #204, Sep , page 3 - Fraktur ( broken ) typeface [the Spanish, for whom the impar5ality of Suez Canal is important due to the Philippine Islands in East Indies, are with suspicious eyes watching the ac5ons of England in Egypt.] 4. Modern Q: Suezin kanava P1. Archaic form: Suetz P2. - InflecHon: kanavan P3. - OCR noise: lanawa

9 Concept [Suez Canal] Expression (multi-word phrase) Referent (Ogden & Richards, 1969) Suez -> suez (in 32 docs) + suezia (4), sueziin (15), sueziin (15), suezin (269) Inflectional mismatches + suetz (2), suetzin (19), suetzista (1), Historical mismatches + sueziu (2), sueziv (2), suezirl (14), suezinkanalva (1), OCR mismatches kanava -> kanava (in 31 docs) + kanavaa (24), kanavalla (28), kanavan (79) Inflectional mismatches + kanawa (358), kanawan (491), kanawalle (29), Historical mismatches + lanawan (139), tanawan (107), fanawan (4) OCR mismatches Historical word forms and OCR noise as added problems

10 1. Inflectional variation In theory, Finnish noun has 1872 forms = 2 (singular/plural) * 13 cases * 6 poss. * 12 clitics even to their canals = kanavilleenkaan In practice (Kettunen & Airio, 2006): Nominative: % Genetive: % Partitive: % Inessive: 6-7 % Still, many possible strings, especially with OCR

11 2. Historical variation Change 1800s vs. modern example Character w w->v waiwa - vaiva Vowel length i->ii pispa piispa Consonant start tr->r trenki renki Vocabulary word change hihhulit lestadiolaiset Utilize systematic character-level variation? How to deal with vocabulary changes? Häkkinen (1994); Järvelin et al. (2015)

12 3. OCR variation Character-level confusions (Raitanen, 2012): Incorrect recognifon Correct (Fraktur) # of Cases m w 162 c e 87 a ä 18 f s 7 Q: Suezin kanava D: Suetzin kanawaa OCR: Suetzin kanamaa

13 Approaches to cope with variation 1. Generation For query word: generate inflections, etc.: kanava -> kanava, kanavan, kanavaa, kanavassa, 2. Reduction For document word: reduce variation to a common form - how to do this?: kanavaan -> kanava kanawaan ->? lanamaan ->? 3. Language-independent Fuzzy string-matching (various)

14 One Study A. Järvelin, H. Keskustalo, E. Sormunen, M. Saastamoinen, K. Kettunen (2015) Information Retrieval From Historical Newspaper Collections in Highly Inflectional Languages Journal of the Assiociation for Information Science and Technology (JASIST) (early view first published online 1 JUN 2015)

15 Two main goals 1. Test Effectiveness of Query Expansion: Add inflections (FCG/Kettunen) (6, 12, 22) Add similar strings (Skip-gram) (5, 20, 30 or 50) 2. Learn about the phenomenon of word variation in noisy historical collections (Finnish)

16 How similar are words? suez -> {su, ue, ez} suetz -> {su, ue, et, tz} à Words share digrams: {su, ue} à Words do not share digrams: {ez, et, tz} Similarity value: sim( suez, suetz ) = {su, ue} / {su, ue, ez, et, tz} = 2/5 = 0.4

17 Selecting best candidates For query word find best candidates from index Due to historical and OCR mismatches: we utilized adjacent and non-adjacent letters (skip-grams): suezin -> {, ue, ez, } suetzin -> {, ue, ez, } ez is evidence about similarity between strings suezin and suetzin -> Select top-n most similar candidates

18 Main Results / QE 1. Best results required extensive expansion (30 variants) 2. Results differ (by topic) but regarding one-word queries, 15 out of 17 were improved by QE (add 30 variants); none was harmed

19 General Level of Performance HIST (N=50) Baseline, unprocessed Htle queries S- grams {{0},{1}{1,2}}, top- 30 MAP (%) * (*) statistically significant difference to the baseline Anni Järvelin et al. (2015) MODERN: Finnish CLEF 2003 (N=45) Inflected, short Fintwol, short, compounds split MAP (%) Kimmo Kettunen & Eija Airio (2006)

20 Main results / Variation Variant candidates were intellectually analyzed For 100 Title words, 20 best candidates of each studied in 8400 page images Four most common cases: (q: vaimo ( wife )) 1. Variant with multiple variation ( waimoin : hist+infl): 26 % 2. Noise ( vaim vain ( only ): OCR): 21 % 3. Variant with inflectional variation ( vaimon ): 19 % 4. Related word with multiple variation ( leskiwaimo : hist +narrower): 12 %

21 Conclusion 1. Noisy historical text offers special challenges related to NER (word-level): Inflectional variation Historical variation (old expressions) Noise (OCR) 2. Q: How to spot relevant strings plagued with such variation? 3. A: Generative, reductive and languageindependent methods can be developed

22 Thank you!

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