XML Corpus Research Articles

Translating Fieldwork into Datasets: The Development of a Corpus for the Quantitative Investigation of Grammatical Phenomena in Eibela

This extended abstract details the process of constructing an annotated XML corpus suitable for quantitative analysis of morphosyntactic and phonetic phenomena in the Eibela language of Papua New Guinea. Preliminary results will also be included, which investigate the semantic, phonetic, and discourse correlates of argument realization. The goal of this paper is to illustrate how legacy materials can be enriched and investigated using computational methodologies including forced alignment of phonetic segments using bulk processing of data in Python and R, the Montreal Forced Aligner (MFA), and morphosyntactic annotation developed as part of the Multilingual Corpus of Annotated Spoken Texts (Multi-CAST).

Machine learning techniques for XML (co-)clustering by structure-constrained phrases

A new method is proposed for clustering XML documents by structure-constrained phrases. It is implemented by three machine-learning approaches previously unexplored in the XML domain, namely non-negative matrix (tri-)factorization, co-clustering and automatic transactional clustering. A novel class of XML features approximately captures structure-constrained phrases as n-grams contextualized by root-to-leaf paths. Experiments over real-world benchmark XML corpora show that the effectiveness of the three approaches improves with contextualized n-grams of suitable length. This confirms the validity of the devised method from multiple clustering perspectives. Two approaches overcome in effectiveness several state-of-the-art competitors. The scalability of the three approaches is investigated, too.

Optimal Probabilistic Generation of XML Documents

We study the problem of, given a corpus of XML documents and its schema, finding an optimal (generative) probabilistic model, where optimality here means maximizing the likelihood of the particular corpus to be generated. Focusing first on the structure of documents, we present an efficient algorithm for finding the best generative probabilistic model, in the absence of constraints. We further study the problem in the presence of integrity constraints, namely key, inclusion, and domain constraints. We study in this case two different kinds of generators. First, we consider a continuation-test generator that performs, while generating documents, tests of schema satisfiability; these tests prevent from generating a document violating the constraints but, as we will see, they are computationally expensive. We also study a restart generator that may generate an invalid document and, when this is the case, restarts and tries again. Finally, we consider the injection of data values into the structure, to obtain a full XML document. We study different approaches for generating these values.

An Application of Topic Map-Based Ontology Generated from Wikipedia for Query Expansion

 Abstract—Topic maps are a Semantic Web technology for semantic annotation of resources to enhance the quality of search output. The main idea of this research is to present a query expansion method using topic maps-based ontology for query expansion process, furthermore this paper proposed a novel automatic approach to construct topic maps from Wikipedia XML corpus. Wikipedia is general purpose, freely available online, is containing up to date information so it is a suitable option for topic map development. The proposed model is implemented and then applied on a test collection. The results show that using topic map-based ontology in query expansion process improves search accuracy in keyword-based information retrieval.

XML document information retrieval model based on four-layered Bayesian network

In this paper,a Bayesian model for XML document information retrieval was proposed,supporting content and structure queries.The topology and conditional probabilities of the Bayesian model were mined from the XML document collection by statistics of terms,elements and structural units contained in the collection.Combined with probability functions,the relevance of a document to a given structured query was obtained by an inference process through a complex dependences network of this model.Experimental results on the INEX corpus of XML documents show the validity and reliability.

XML 문서의 구조기반 검색성능 평가

In extension to our previous study, we develop metadata that specify elements` structural orders, to increase the efficiency level of XML document`s retrieval process. Then, we proposed a structure-based indexing model. We expect the model to generate a more efficient retrieval process of horizontally and vertically related elements. To evaluate the model`s performance level, we developed an experimental prototype and conducted an experiment on an XML corpus. On average, descendant, ancestor and sibling retrievals were approximately twelve percent faster than the ETID model. And retrievals specifying structural orders of particular element types were approximately twenty-five percent faster than the ETID model. In conclusion, metadata, such as Etype, Asso and Lsso, may make a meaningful contribution to retrieval processes that specify elements` order.

XML compression techniques: A survey and comparison

XML has been acknowledged as the defacto standard for data representation and exchange over the World Wide Web. Being self describing grants XML its great flexibility and wide acceptance but on the other hand it is the cause of its main drawback that of being huge in size. The huge document size means that the amount of information that has to be transmitted, processed, stored, and queried is often larger than that of other data formats. Several XML compression techniques has been introduced to deal with these problems. In this paper, we provide a complete survey over the state-of-the-art of XML compression techniques. In addition, we present an extensive experimental study of the available implementations of these techniques. We report the behavior of nine XML compressors using a large corpus of XML documents which covers the different natures and scales of XML documents. In addition to assessing and comparing the performance characteristics of the evaluated XML compression tools, the study also tries to assess the effectiveness and practicality of using these tools in the real world. Finally, we provide some guidelines and recommendations which are useful for helping developers and users for making an effective decision towards selecting the most suitable XML compression tool for their needs.

Extractive summarisation of legal texts

We describe research carried out as part of a text summarisation project for the legal domain for which we use a new XML corpus of judgments of the UK House of Lords. These judgments represent a particularly important part of public discourse due to the role that precedents play in English law. We present experimental results using a range of features and machine learning techniques for the task of predicting the rhetorical status of sentences and for the task of selecting the most summary-worthy sentences from a document. Results for these components are encouraging as they achieve state-of-the-art accuracy using robust, automatically generated cue phrase information. Sample output from the system illustrates the potential of summarisation technology for legal information management systems and highlights the utility of our rhetorical annotation scheme as a model of legal discourse, which provides a clear means for structuring summaries and tailoring them to different types of users.

SAGAXSEARCH: AN XML INFORMATION RETRIEVAL MECHANISM USING SELF ADAPTIVE GENETIC ALGORITHMS

The XML technology, with its self-describing and extensible tags, is significantly contributing to the next generation semantic web. The present search techniques used for HTML and text documents are not efficient when retrieving relevant XML documents. In this paper, Self Adaptive Genetic Algorithms are presented to learn about the tags, which are useful in indexing. The indices and relationship strength metric are used to extract fast and accurate semantically related elements in the XML documents. The Experiments are conducted on the DataBase systems and Logic Programming (DBLP) XML corpus and are evaluated for precision and recall. The proposed SAGAXsearch outperforms XSEarch3 and XRank20 with respect to accuracy and query execution time.

A Bayesian Framework for XML Information Retrieval: Searching and Learning with the INEX Collection

Most recent document standards like XML rely on structured representations. On the other hand, current information retrieval systems have been developed for flat document representations and cannot be easily extended to cope with more complex document types. The design of such systems is still an open problem. We present a new model for structured document retrieval which allows computing scores of document parts. This model is based on Bayesian networks whose conditional probabilities are learnt from a labelled collection of structured documents--which is composed of documents, queries and their associated assessments. Training these models is a complex machine learning task and is not standard. This is the focus of the paper: we propose here to train the structured Bayesian Network model using a cross-entropy training criterion. Results are presented on the INEX corpus of XML documents.