1. Document Centered Approach to Text Normalization Andrei Mikheev LTG University of Edinburgh SIGIR 2000

2. Abstract Three problems of text normalization: n Sentence Boundary Disambiguation (SBD) n Disambiguation of capitalization when words are used in positions where capitalization is expected n Identification of abbreviations Use of the Document Centered Approach methods to reduce sentence boundary disambiguation with pre-built resources from existing corpora (i.e. Wall Street Journal and Brown)

3. Introduction n Text cleaning and normalization is used to develop text processing and Information Retrieval applications. –Text normalization begins with disambiguation of capitalized words Capitalization is expected for proper names, locations, people etc. Ambiguity is presented with mandatory rule of capitalization in special positions (e.g., at the start of a sentence) n Disambiguation of capitalized words in ambiguous positions (also known as normalization) leads to identification of proper names –Study conducted by Church reflects: reference to same thing/object (e.g., hurricane and Hurricane) reference to different thing/object (e.g., apple [fruit] and Apple [computer])

4. Introduction (cont.) n Disambiguation serves toward sentence splitting/sentence boundary disambiguation (SBD) –Sentence splitting: the process of creating a sentence boundary using punctuation such as “!”, “?”, “.” –Periods can serve one or several roles at once: splitting text information denoting decimal points denoting an abbreviation

5. Our Approach to SBD (Sentence Boundary Disambiguation) n Experiment began with use of Wall St. Journal corpus and the Brown corpus –Three tasks involved with both corpora Sentence Boundary Disambiguation Capitalized Word Disambiguation Abbreviation Identification –Human-involved, labor-intensive programming of both corpora to recognize: abbreviations that are followed by proper names (e.g., Mr. White) abbreviations which are single-word, short and, in most cases, do not include vowels (e.g. kg., ft., etc.) abbreviations consisting of a series of capitalized single letters separated by periods (e.g., Y.M.C.A., U.C.L.A., A.L.A., etc.) –With programming, error rates still proved to be too high within both corpora (15-16% error rate)

6. Document-Centered Approach n Document-Centered Approach (DCA): reviewing entire document to formulate disambiguation as it relates to capitalization of proper names and abbreviations –Generalized Principles for the DCA method: if a word has been capitalized in an unambiguous position, this increases the probability that it is a proper name (e.g., “The Riders [as in the Rider family] said….”) if a short word (e.g., “in.” standing for inches) is followed by a period, but occurs elsewhere in the document without a period, the likelihood is that it is not an abbreviation

7. Getting Abbreviations n Recognition of abbreviations process begins with using existing abbreviation lists n Enhancing existing abbreviation lists (which may be incomplete for existing document) by: –collecting unigram forms of abbreviations from existing document (e.g., Sun., which can stand for Sunday or the newspaper) –collecting bigram forms of abbreviations, which are made up of two words but recognized as one word (e.g., “Vitamin C.”)

8. Getting Capitalized Words n Disambiguation of capitalized words using the following methods: – The Sequence Strategy: the process of exploring sequences of proper nouns in unambiguous positions – Frequent List Lookup Strategy: a pre-programmed compilation of words that are frequently capitalized to denote proper names – Single Word Assignment: the process of reviewing the entire document to determine whether capitalized words in the document act as proper names – The “After Abbr.” Heuristic: the process of determining a proper name, when a capitalized word follows a capitalized abbreviation, the capitalized word is, in most cases, certainly a proper name

9. Getting Capitalized Words (cont.) n The Overall Performance: upon applying the four methods above, the final disambiguation results were: –9% ambiguously capitalized words unclassified in Brown Corpus –15% ambiguously capitalized words unclassified in Wall Street Journal Corpus n Ranking of the methods achieving best results for classifying ambiguously capitalized words: –Single Word Assignment –“After Abbr.” Strategy –Sequence Strategy

10. Assigning Sentence Breaks n The process of correctly recognizing the end of an idea, thought or statement, where a small, and in some cases, non-voweled word followed by a period and then by a lower cased word, we can assume that the small word is an abbreviation

11. Related Research n Two types of existing SBD systems: –Rule based system: A system comprised of manually built rules, encoded with lists of proper names, abbreviations, common words, etc. to recognize where sentences break within a document –Machine learning system: A system employing the use features such as word spelling, capitalization, suffix, word class, etc. to recognized potential sentence breaking punctuation Examples of machine learning systems developed by: –Kuhn and de Mori –Clarkson and Robinson –Mani&MacMillan –Gale, Church and Yarowsky

12. Discussion n Final results of the Document Centered Approach: –This approach proved to be comparable to or even better than existing approaches –This approach does not require any human intervention for training –Simplicity of this approach resulted in high-running speed –This approach does not rely on pre-compiled statistics –Easy implementation, without installing new software –This system can be customized for specific domain usage