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Measuring Monolinguality for Language Quality Assurance

Explore techniques to measure monolinguality for language resources, clean corpora from alien language noise, and maintain resource quality. Discover significance of monolinguality in NLP tasks and methods. Study results and applications for monolingual corpora. Experiment with artificial noise mixtures and corpus cleaning to evaluate language quality. Benefit from language statistics, web crawling techniques, and corpus cleaning methods. Full guidance for text analysis, classification, and language resource management for improved language quality.

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Measuring Monolinguality for Language Quality Assurance

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  1. Measuring Monolinguality Chris Biemann NLP Department, University of Leipzig LREC-06 Workshop on Quality Assurance and Quality Measurement for Language and Speech Resources, Genova 27 May 2006

  2. Why Monolinguality ? Alien language noise disturbs statistics for corpus-based methods: • Language Models, e.g. n-gram • Lexical Acquisition • Semantic Indexing • Co-occurrence Statistics

  3. What is Monolinguality? • Foreign language sentences should be removed • Sentences containing few foreign language words or phrases, such as movie titles, terminology etc. should remain.

  4. Korean Example • A:Yes. The traffic cop said I had one too many and made me take the sobriety test, but I passed it. B:Lucky you ! • 무인도 표류 소년 25명 통해 인간의 야만성 그려 영국 소설가 윌리엄 골딩의 83년 노벨문학상 수상작을 영화화한 `파리대왕'(Lord of the flies)은 결코 편안하게 감상할 수 있는 영화는 아니다 .

  5. Recall Zipf‘s Law It holds also for random samples of words Top frequent words

  6. Measuring Monolinguality Given a corpus of language A with x% noise of language B, the amount of noise is measured: • For top frequency words of B, divide the relative frequency in the corpus by the relative frequency of a clean B corpus • The amount of noise is the predominant ratio: many ratios will be close to x%.

  7. The top frequency words of B w.r.t. A • Words that do not occur in language A. Their frequency ratio will be around x%. • Words that are also amongst the highest frequency words of language A and moreover have the same function. Their frequency ratio will be around 1. • Words that occur in language A, but at different frequency bands. They are a random sample of words of L and distributed in a Zipf way • Words of B that are often used in named entities and titles (such as capitalized stop words). They appear in the corpus of language A more frequently then the expected x% of noise. The second group of words is only present in languages that are very similar to each other.

  8. Lexical overlap in top 1000 words

  9. Experiment 1 Artificial noise mixtures: Injecting alien language material in monolingual corpora • Experiment 1a: Injecting different amounts of German Noise in a chunk of the British National Corpus (~ 20 Million words) • Experiment 1b: Injecting 1% noise of Norwegian, Swedish and Dutch into a Danish corpus (~17 Million words) For measuring, we used the top 1000 words

  10. German in BNC

  11. Invading Denmark

  12. Before cleaning After cleaning Number of top-1000-words found Approx. Frequency ratio Number of top-1000-words found Frequency ratio German 1000 0.708 1000 0.946 English 995 0.126 987 0.0010 French 924 0.0398 906 0.00002 Dutch 995 0.000891 775 0.000006 Turkish 642 0.0000631 562 0.000006 Experiment 2 For a collection of web documents (~700 Million words from .de domains, we measure the effect of a corpus cleaning method that strips alien language material

  13. Cleaning .de web

  14. Conclusion • Measure captures well the amount of noise • Noise measured down to a ratio of 10-5 • Effective: involves 1000 frequency counts per language

  15. Application: Monolingual Corpora • Screenshot corpora http://corpora.uni-leipzig.de

  16. Workflow Texts: Web / Newspapers Dictionaries (Dornseiff, WordNets, Wikipedia, ...) Small Worlds URLs Crawling Small Worlds Clustering Classification Words Text Text Text Text • Similar objects (words, sentences, documents, URLs) • Classification (se-mantic properties, subject areas, ...) • Combined objects (NE-Recognition, terminology, ...): determine patterns,extract multi-words Resources Techniques Results Language detection, Cleaning • Decomposition • Morphology • Inflection • Translation pairs lang. 1 lang. 2 lang. n ... Language +Time Tools Co-occurrences etc. POS Tagging • Neologisms • Trend Mining • Topic Tracking Standard Size Corpora Web Statistics Dictionaries Classified Objects Language Statistics Small Worlds

  17. CorpusBrowser Per word: • Frequency • Example sentences • Co-occurrences: left and right neighbours, sentence-based • Co-occurrence graph

  18. Only a few copies left! DVD: • 15 languages • Corpus Browser • Corpora in plain text and database format

  19. Questions?? THANK YOU!

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