Preprocessing in Cyberlaw Web Knowledge Base Construction

This paper describes a functional model for preprocessing and preparing natural language text from online news documents for constructing knowledge base. The model involves five phases of processing which includes tokenizing free text into segments, identifying syntactic categories (i.e. part-of-speech and phrasal categories) and grammatical relations (e.g. subject, object and etc), tagging noun phrases with named-entity identifiers, analyzing morphological root of verb phrases and resolving pronouns. 1 The Motivation The functional approach towards syntactic preprocessing in this paper is part of a Cyberlaw knowledge base construction from online news documents [1]. Our focus is directed to the Cyberlaw domain as such effort to extract and transform news related to legal activities in the Information Technology and Internet world in order to create and maintain a knowledge base has never been attempted before. Moreover, as more parts of our life become acquainted to technology, this knowledge base will be a valuable resource for legal practitioners, legal students, academicians and others alike. Our belief that a more precise knowledge representation cannot be achieved without attempting a deeper understanding of at least parts of the text has also prompted us to embark in the study of approaches in the syntax level. As a subfield of linguistics, syntax deals with the patterned relations that govern the way words in a sentence come together. It concerns how different words which are categorized as nouns, adjectives, verbs and etc are combined into clauses which in turn combine into sentences. In order to scrutinize how the syntactic structure of a sentence can be computed, one must consider two things namely the grammar, which is a formal specification of the structures permissible in the language and the parsing technique, which is the method of analyzing a sentence to establish its structure according to the grammar. In the terms of knowledge base construction, syntactic preprocessing lays the ground work required for transforming free text meant for human conception to a machine conceivable format. It involves procedures like segmenting multi-paragraph text, tagging each word with syntactic categories and grammatical relationships, recognizing named-entities, analyzing words for their morphological roots, disambiguating words and etc. And despite these responsibilities, there is no complete mechanism for all these tasks that provides definite solutions of 100% accuracy. But nonetheless, many researchers have produced algorithms and formalisms that provide very promising results as we will see later. These physical works lay a solid foundation for us to outline the various functional requirements and considerations when building syntactic preprocessor tailored for knowledge base construction. 2 Related Research The work in syntactic preprocessing can be broadly grouped into two solutions. An integrated solution like what has been carried out by LaSIE (Large Scale Information Extraction) [2] and FASTUS (Finite State Automata-based Text Understanding System) [3]. The other class of approach is independent studies like Link Grammar [4], Minipar [5], Brill tagger [6], MUSE [7, 11] and etc which deals with different sections of syntactic preprocessing. LaSIE is by far the most complete system offering an integrated solution to syntactic preprocessing as part of its information extraction responsibility. The processing consists of splitting and tokenizing the input, part-of-speech tagging morphological analysis and namedentity tagging. FASTUS, a somewhat misleading acronym is actually another information extraction system. The system consists of a cascade of transducers, namely tokenizer, preprocessor, phrase parser and phrase combiner. Link grammar is a relatively new formal grammatical system for parsing English sentences. The formalism is lexical and does not make use of constituents and categories. Minipar is a principle-based English parser. It represents its grammar as a network where nodes represent grammatical categories and links represent types of syntactic relations. Brill tagger is a rule-based part-of-speech tagger which automatically acquires its rules as opposed to conventional rule-based tagger and thus, performs as well as those based upon probabilistic models like Markov model. MUSE is a multi-purpose named entity recognition system consisting of three main processing phases. A tokenizer which splits text into atomic level producing numbers, punctuation, symbols and words, a gazetteer which consists of lists of proper nouns and triggers, and a grammar which constitutes of manually produced rules describing patterns to match. 3 Functional Overview The processing resources required for outlining an integrated approach in syntax preprocessing varied depending on the requirements of the system to which the approach is attached to. The choice between a part-of-speech tagger and sentence parser is very much dependant on the type of output required by the next processing phase. For example, if the system does not require grammatical categories in the output, then using a sentence parser is just redundant. The input to the syntactic preprocessor is an online news document which may consist of several paragraphs. The functional model of the preprocessing phases required is shown in Figure 2 as part of a knowledge base construction system in Figure 1. categorizer trainer www semantic parser & interpreter learner state training set gazetteer crawler config news syntactic preprocessor crawler wrapper categorizer crawler log semantic network inference rule ontology metadata