Proceedings - Österreichische Gesellschaft für Artificial Intelligence

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Comparison of Named Entity Recognition tools for raw OCR text Kepa Joseba Rodriquez Göttingen State and University Library rodriguez@ sub.uni-goettingen.de Mike Bryant Centre for e-Research King’s College London michael.bryant@ kcl.ac.uk Tobias Blanke Centre for e-Research King’s College London tobias.blanke@ kcl.ac.uk Magdalena Luszczynska University College London zclhd0@ ucl.ac.uk Abstract This short paper analyses an experiment comparing the efficacy of several Named Entity Recognition (NER) tools at extracting entities directly from the output of an optical character recognition (OCR) workflow. The authors present how they first created a set of test data, consisting of raw and corrected OCR output manually annotated with people, locations, and organizations. They then ran each of the NER tools against both raw and corrected OCR output, comparing the precision, recall, and F1 score against the manually annotated data. 1 Introduction 1 While the amount of material being digitised and appearing online is steadily increasing, users’ ability to find relevant material is constrained by the search and retrieval mechanisms available to them. Free text search engines, while undoubtedly of central importance, are often more effective and friendly to users when combined with structured classification schemes that allow faceted search and iterative narrowing-down of large result sets. Such structured classification, however, is costly in terms of resources. Named Entity Recognition (NER) holds the potential to lower this cost by using natural language processing tools to automatically tag items with people, places, and organizations that may be used as access points. 1 A version of this paper was presented in the proceedings of DH2012. The European Holocaust Research Infrastructure (EHRI) 2 project aims to create a sustainable research infrastructure bringing together resources from dispersed historical archives across different countries. As part of this effort, we are investigating if and how we can enhance the research experience by offering tools to EHRI project partners that enable them to semantically enrich sources that they contribute to the wider infrastructure. A component of these services is currently planned to be a flexible OCR service based on the workflow tools developed at King’s College London for the JISC-funded Ocropodium project (Blanke et al., 2011). While OCR itself is a complex process that produces hugely variable results based on the input material, modern tools are capable of producing reasonable transcripts from mid 20th-century typewritten material that is quite common among the EHRI project’s primary sources. The quality of these transcripts prior to correction would be considered quite low for human readers, but even when uncorrected can offer value for search indexing and other machine processing purposes (Tanner et al., 2009). This experiment was undertaken to evaluate the efficacy of some available tools for accurately extracting semantic entities that could be used for automatically tagging and classifying documents based on this uncorrected OCR output. Section 2 briefly summarises the existing literature covering the use of OCR and named entity extraction in the field of historical research. Section 3 describes our methodology for scoring each of the trialled NER tools in terms of pre- 2 http://www.ehri-project.eu 410 <strong>Proceedings</strong> of KONVENS 2012 (LThist 2012 workshop), Vienna, September 21, 2012
cision, recall, and F1. Section 4 describes the materials on which we conducted the experiment. Section 5 presents the results of our experiment and some analysis of those results, while section 6 concludes and discusses the research we intend to conduct in the future. 2 Literature Review Grover et al. (2008) describe their evaluation of a custom rule-based NER system for person and place names on two sets of British Parliamentary records from the 17th and 19th centuries. They describe many of the problems encountered by NER systems that result from both OCR artefacts and the archaic nature of the sources themselves, such as conflation of marginal notes with body text, multi-line quoting rules, and capitalization of common nouns. Packer et al. (2010) tested three different methods for the extraction of person names from noisy OCR output, scoring the results from each method against a hand-annotated reference. They noted a correlation between OCR word error rate (WER) and NER quality did exist, but was small, and hypothesised that errors in the OCR deriving from misunderstanding of page-level features (i.e. those that affect the ordering and context of words) have a greater impact on NER for person names than character-level accuracy. 3 Methodology The goal of our experiment is the evaluation of the performance of existing NER tools on the output of an open source OCR system. We have selected four named entity extractors: a) OpenNLP 3 b) Stanford NER (Finkel et al., 2005) 4 c) AlchemyAPI 5 d) OpenCalais 6 The entity types we chose to focus on were person (PER), location (LOC) and organization (ORG). These entity types are the most relevant 3 http://opennlp.apache.org 4 http://nlp.stanford.edu/software/ CRF-NER.shtml 5 http://www.alchemyapi.com 6 http://www.opencalais.com for the extraction of metadata from documents in the domain of the EHRI project, and have a good coverage by the selected tools. The named entity extractors use different tagsets for the annotation of named entities. We have selected the different categories and normalized the annotation as follows: a) Stanford NER and OpenNLP: Person, location and organization categories have been annotated for all used models. b) OpenCalais: we have merged the categories ”Country”, ”City” and ”NaturalFeature” into the category LOC, and the categories ”Organization” and ”Facility” into the category ORG. This second merging was possible because in our domain almost all facilities that appear were official buildings and public facility buildings referenced in an organizational capacity. c) Alchemy: We have merged the categories ”Organization”, ”facility” and ”Company” into the category ORG and ”Country” , ”City” and ”Continent” into LOC. To our knowledge there is at the moment no freely available corpus that can be used as gold standard for our purposes. For this reason we have produced a manually annotated resource with text extracted from images of documents related to the domain of the EHRI project as described in section 4. We have compared the output of the tools with this standard and computed the precision, recall and F1. 4 Experimental Setup The material used for our initial experiments was obtained from the Wiener Library, London, and King’s College London’s Serving Soldier archive. The Wiener Library material consisted of four individual Holocaust survivor testimonies, totalling seventeen pages of type-written monospaced text. Since the resolution of the scans was low and the images quite “noisy” (containing hand-written annotations and numerous scanning artifacts) we did not expect the quality of the OCR to be high, but felt they were suitably representative of material with which we expected to be dealing with in the EHRI project. 411 <strong>Proceedings</strong> of KONVENS 2012 (LThist 2012 workshop), Vienna, September 21, 2012
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11 th Conference on Natural Languag
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Table of Contents Main conference P
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Comparing variety corpora with vis-
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LexSem 2012: Workshop on Recent Dev
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KONVENS 2012 The 11 th Conference o
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Organizers: Jeremy Jancsary (ÖGAI/
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Ines Rehbein (Potsdam University) M
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Integrating viewpoints into newspap
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Phonetically aided syntactic parsin
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The Statistical Approach to Natural
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Data-driven Knowledge Extraction fo
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4 The Different Relations Types In
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Relation Type #Patterns Examples Su
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Suits-to Served-with Substituted-by
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References Timothy Chklovski and Pa
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1.2 Problem Definitions In a MRA, t
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oot person organisation product ...
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Group Organis. Persons Products all
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Method α α+β α+γ all SentiWS 0
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A Supervised POS Tagger for Written
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Our tagset mixes and matches tags a
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ecause the enclitic pronoun is an i
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stochastic rules as opposed to larg
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Kevin Duh, and Katrin Kirchhoff. 20
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forms, and that existentials can be
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Pseudo Verb Punctuation PNC
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NLP Workflow for On-line Definition
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nological phrases on the basis of p
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Figure 2: A screenshot of the entir
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When analyzing the evaluation sets
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Projecting Semantic Roles via Tai M
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quent prediction, return it (iii) i
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for the B/T/FT settings. This is pr
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ut it is implied by Table 3 and Fig
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References Wanxiang Che, Zhenghua L
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Our ultimate goal is to develop com
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ADORNING CAUSE_FLUIDIC_MOTION COTHE
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English nouns, verbs, adjectives, a
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considerably worse than FrameNet. T
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Extraction. In Proceedings of the 1
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lexicon from a document-derived voc
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Initialize hypernym set H to contai
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ulary distance. Let d(a, b) denote
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the reduced lexicon (243) was neare
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Adding nominal spice to SALSA - fra
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SALSA verbs nouns 0 FE 0 2 1 FE 68
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gen, which in one of its meanings i
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In the next section we describe how
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Acknowledgments This work was funde
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most commonly used (Turney and Pant
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anteed because the forward feature
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5 000 feat ICA50 SVD50 SENNA Semcat
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words bicycle and tricycle go with
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of the Twelfth European Conference
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incorporate the context information
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those candidate phrases that are an
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5.2 Evaluation Metrics for WPD The
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Runs F-measures α=0.5 α=0.2 Pur.
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Evaluations (SemEval-2007), June (2
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2 Related Work The majority of pape
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and Roth, 2009; Lin and Wu, 2009).
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Features Dev Test CoNLL-2003 w 0 +
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Feature Dev Test Test+ — 93.78 91
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the Joint Conference of the 47th An
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tion. It was first included as a sp
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indicates the presence of the ellip
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[Armada britainiarrak] [Ipar Irland
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Información, (JOTRI 2003), pages 1
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Phonetically aided Syntactic Parsin
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mar network or PCFG. Grammar networ
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phonetic confusion that has the con
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in confusion in the network. This i
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Linguistic analyses of the LAST MIN
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Table 1: Comparison between corpora
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Table 2: Most frequent POS patterns
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Table 3: A problematic dialog situa
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6 Summary We have presented the cur
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S-restricted monotone alignments St
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s k n i i: f phh ohehnh ih xh s k n
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Dataset µ G µ P P [G>15] P [P >15
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Perplexity H(L | P ) 2PKE-Uni 7.002
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(1, 1) (2, 1) (3, 1) (4, 1) (1, 2)
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Use of linguistic features for impr
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whereas English has a verb-initial
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Accuracy of Accuracy of Accuracy of
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English Persian Train: Sentences 30
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to compensate for sparsity of paral
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Name # Documents # Tokens # Lemmas
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Figure 1: Precision-Recall graphs c
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A Multi-level Annotation Model for
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and sentence level with the aim of
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(Schmid, 1995; Schiller et al., 199
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EI EII Tag AKR MARK Typography ITER
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References Saima Aman and Stan Szpa
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Robust processing of noisy web-coll
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1. first strip of the papery skin o
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References Jon Chamberlain, Massimo
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ments between its LSRs. Additionall
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Figure 3: In the sense comparison v
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Using Information Retrieval Technol
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against every tree. Others avoid th
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A 0.780715 NN Figure 4: A sp
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# #Documents Index Size 1 29,704 1.
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References P. Bański, C. Belica, H
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(2012), in their corpus study of va
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DET NSUBJ ADV OBJA AMOD Seine Tante
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A Computational Semantic Analysis o
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(6) a. Is Spain very large? b. Is B
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References Alan C. Bale. 2008. A Un
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219 Proceedings of KONVENS 2012 (Ma
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221 Proceedings of KONVENS 2012 (Ma
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Using subcategorization frames to i
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5.2 Word clustering based on the Le
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erage of english grammars. In Proce
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cases not disambiguated using langu
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Not surprisingly, the results show
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Automatic Identification of Languag
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Differences also appear at the synt
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Acknowledgments The authors express
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2 Related Work Previous work on POS
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The XY tag is used for all non-word
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Comparing Variety Corpora with Vis-
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INPUT variety corpus comparability
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References Andrea Abel and Stefanie
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Features Context features (token wi
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0.7 0.7 0.6 0.6 0.5 0.5 F-measure 0
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ties into fine-grained classes. In
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verbials (e.g., therefore, still).
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word raw freq. non conn. conn. (of
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Corpus-based Acquisition of German
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Events Objects IPrec CPrec IPrec CP
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References Marco Baroni, Silvia Ber
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Preface The present proceedings con
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Workshop Program Friday, September
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Unsupervised Sentiment Analysis wit
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pling (Geman and Geman, 1984) to es
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References Anders Björkelund, Bern
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Class # Events in % neutral 45’01
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3.1.1 Rules Let t 1..n be tokens of
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Human 6 System 6 Human 4 System 4 A
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Codebuch Akteursanalyse. rev. 2011.
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of our work in the whole applicatio
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3.4 Negation and graduation Valence
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local context. For instance, in une
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n’était pas très favorable au G
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Domain-specific variation of sentim
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query building blocks comments real
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References Monica Bednarek. 2006. E
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3 Basic Methodology of Annotation T
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(3) A. V případě jeho kandidatur
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4.3 Auxiliary data - Mall.cz We hav
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Overlap f-score Source 0.750 Polar
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A Phrase-Based Opinion List for the
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the Stuttgart-Tübingen Tagset (STT
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As for noun-based opinion phrases a
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More than half of the words and phr
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treebank. In Proceedings of the Wor
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Apart from these, we distinguish op
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close variants)”. In fact importa
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What is the Meaning of 5 *’s? An
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For each category, we follow (Const
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Positivity Term 2.87692577130 elske
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In recent years, the use of rating
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KONVENS 2012 LThist 2012: First Int
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Organisation Committee: Thierry Dec
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Workshop Program Friday, September
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Rule-Based Normalisation of Histori
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isation rules were applied to the i
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ments in the gold standard have bee
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cation in the contemporary SUC samp
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proach. References Maria Ågren, Ro
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2 Related Work There has been consi
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vided to ensure that they are norma
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alects, or even languages. Norma ca
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ization (rather than modernization)
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The Sketch Engine as infrastructure
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2.1 The Corpus of English Dialogues
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Other notable contrasts are that 17
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Evaluating a Post-editing Approach
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Page 423 and 424: References Harald R. Baayen, R. Pie
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Page 439 and 440: verbs were collected mostly from an
Page 441 and 442: 3 Theoretical Background Let us now
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Källström and Inger Lindberg, edi
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scoring function σ : W ×L×W →
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esults in the triple 〈heutige Son
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Unlexicalized links 7,833,635 n (i)
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The difference in accuracies betwee
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Towards high-accuracy bilingual phr
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over a set of candidate translation
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each soft restriction soft m set on
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(> 98% in scalable precision) and a
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References Colin Bannard and Chris
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Preface The need for automatically
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15:50-16:15 Coffee break 16:15-16:4
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Standards for the technical infrast
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⎡ ⎤ phrase CATEGORY np ⎡ ⎤
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The representation of the feature v
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ement, while the interpretation as
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hand, checking the validity of FSR
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Standardizing metadata descriptions
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2 LMF and semantic interoperability
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can register and create DCs. Users
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all kinds of lexemes, (see Cruse (1
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constraints, because many aspects o
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Menzo Windhouwer. 2012. RELcat: a R
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the meeting in Rome as a starting p
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As these documents provide the refe
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Two multi-layer annotated corpora r
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As audio data is not directly store
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Jörg Mayer. 1995. Transcription of
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Towards standardized lexical semant
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Getting involved into language reso
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