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

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

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.

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 .

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

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%.

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.

Lexical overlap in top 1000 words

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

German in BNC

Invading Denmark

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   Before cleaning After cleaning Number of top-1000-words found Approx. Frequency ratio Frequency ratio German 1000 0.708 0.946 English 995 0.126 987 0.0010 French 924 0.0398 906 0.00002 Dutch 0.000891 775 0.000006 Turkish 642 0.0000631 562

Cleaning .de web

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

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

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 Classified Objects Dictionaries Language Statistics Small Worlds

Corpus Browser Per word: Frequency Example sentences Co-occurrences: left and right neighbours, sentence-based Co-occurrence graph

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

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