Concept-based Interactive Query Expansion Support Tool (CIQUEST)

This report describes a three-year project (2000-03) undertaken in the Information Studies Department at The University of Sheffield and funded by Resource, The Council for Museums, Archives and Libraries. The overall aim of the research was to provide user support for query formulation and reformulation in searching large-scale textual resources including those of the World Wide Web. More specifically the objectives were: to investigate and evaluate methods for the automatic generation and organisation of concepts derived from retrieved document sets, based on statistical methods for term weighting; and to conduct user-based evaluations on the understanding, presentation and retrieval effectiveness of concept structures in selecting candidate terms for interactive query expansion. The TREC test collection formed the basis for the seven evaluative experiments conducted in the course of the project. These formed four distinct phases in the project plan. In the first phase, a series of experiments was conducted to investigate further techniques for concept derivation and hierarchical organisation and structure. The second phase was concerned with user-based validation of the concept structures. Results of phases 1 and 2 informed on the design of the test system and the user interface was developed in phase 3. The final phase entailed a user-based summative evaluation of the CiQuest system. The main findings demonstrate that concept hierarchies can effectively be generated from sets of retrieved documents and displayed to searchers in a meaningful way. The approach provides the searcher with an overview of the contents of the retrieved documents, which in turn facilitates the viewing of documents and selection of the most relevant ones. Concept hierarchies are a good source of terms for query expansion and can improve precision. The extraction of descriptive phrases as an alternative source of terms was also effective. With respect to presentation, cascading menus were easy to browse for selecting terms and for viewing documents. In conclusion the project dissemination programme and future work are outlined

Usefulness, localizability, humanness, and language-benefit: additional evaluation criteria for natural language dialogue systems

Human–computer dialogue systems interact with human users using natural language. We used the ALICE/AIML chatbot architecture as a platform to develop a range of chatbots covering different languages, genres, text-types, and user-groups, to illustrate qualitative aspects of natural language dialogue system evaluation. We present some of the different evaluation techniques used in natural language dialogue systems, including black box and glass box, comparative, quantitative, and qualitative evaluation. Four aspects of NLP dialogue system evaluation are often overlooked: “usefulness” in terms of a user’s qualitative needs, “localizability” to new genres and languages, “humanness” or “naturalness” compared to human–human dialogues, and “language benefit” compared to alternative interfaces. We illustrated these aspects with respect to our work on machine-learnt chatbot dialogue systems; we believe these aspects are worthwhile in impressing potential new users and customers

Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

PLPrepare: A Grammar Checker for Challenging Cases

This study investigates one of the Polish language’s most arbitrary cases: the genitive masculine inanimate singular. It collects and ranks several guidelines to help language learners discern its proper usage and also introduces a framework to provide detailed feedback regarding arbitrary cases. The study tests this framework by implementing and evaluating a hybrid grammar checker called PLPrepare. PLPrepare performs similarly to other grammar checkers and is able to detect genitive case usages and provide feedback based on a number of error classifications

Semantic and pragmatic motivations for constructional preferences: a corpus-based study of provide, supply, and present

A select group of transfer verbs can enter into four different constructions: the ditransitive construction (He provided John the money), the prepositional-dative construction (He provided the money to John), a construction with a prepositional theme (He provided John with the money), and a construction with a recipient realized by a for-phrase (He provided the money for John). In this article, we take a close look at three such verbs: provide, supply, and present. Corpus analysis shows that these three verbs display different structural preferences with respect to the for-, to-, and with-patterns. To explain these preferences, the study investigates pragmatic principles (following Mukherjee 2001 on provide) and the role played by semantic factors. An examination of the semantics of the verbs and the lexically motivated constructional semantics of the to, for, and with-patterns shows (i) that the three constructions are not interchangeable, and (ii) that the preferential differences between the three verbs find an explanation in the compatibility between lexical and constructional semantics. The description is mainly based on data from the British National Corpus

Learning to Extract Keyphrases from Text

Many academic journals ask their authors to provide a list of about five to fifteen key words, to appear on the first page of each article. Since these key words are often phrases of two or more words, we prefer to call them keyphrases. There is a surprisingly wide variety of tasks for which keyphrases are useful, as we discuss in this paper. Recent commercial software, such as Microsoft?s Word 97 and Verity?s Search 97, includes algorithms that automatically extract keyphrases from documents. In this paper, we approach the problem of automatically extracting keyphrases from text as a supervised learning task. We treat a document as a set of phrases, which the learning algorithm must learn to classify as positive or negative examples of keyphrases. Our first set of experiments applies the C4.5 decision tree induction algorithm to this learning task. The second set of experiments applies the GenEx algorithm to the task. We developed the GenEx algorithm specifically for this task. The third set of experiments examines the performance of GenEx on the task of metadata generation, relative to the performance of Microsoft?s Word 97. The fourth and final set of experiments investigates the performance of GenEx on the task of highlighting, relative to Verity?s Search 97. The experimental results support the claim that a specialized learning algorithm (GenEx) can generate better keyphrases than a general-purpose learning algorithm (C4.5) and the non-learning algorithms that are used in commercial software (Word 97 and Search 97)

Coptic SCRIPTORIUM:A Corpus, Tools, and Methods for Corpus Linguistics and Computational Historical Research in Ancient Egypt

Coptic, having evolved from the language of the hieroglyphs of the pharaonic era, represents the last phase of the Egyptian language and is pivotal for a wide range of disciplines, such as linguistics, biblical studies, the history of Christianity, Egyptology, and ancient history. Coptic SCRIPTORIUM provides the first open-source technologies for computational and digital research across the disciplines as applied to Egyptian texts. The project is developing a digitized corpus of Coptic texts available in multiple formats and visualizations (including TEI XML), tools to analyze and process the language (e.g., the first Coptic part-of-speech tagger), a database with search and visualization capabilities, and a collaborative platform for scholars to contribute texts and annotations and to conduct research. The technologies and corpus will function as a collaborative environment for digital research by any scholars working in Coptic

A Study Of Data Informatics: Data Analysis And Knowledge Discovery Via A Novel Data Mining Algorithm

Frequent pattern mining (fpm) has become extremely popular among data mining researchers because it provides interesting and valuable patterns from large datasets. The decreasing cost of storage devices and the increasing availability of processing power make it possible for researchers to build and analyze gigantic datasets in various scientific and business domains. A filtering process is needed, however, to generate patterns that are relevant. This dissertation contributes to addressing this need. An experimental system named fpmies (frequent pattern mining information extraction system) was built to extract information from electronic documents automatically. Collocation analysis was used to analyze the relationship of words. Template mining was used to build the experimental system which is the foundation of fpmies. With the rising need for improved environmental performance, a dataset based on green supply chain practices of three companies was used to test fpmies. The new system was also tested by users resulting in a recall of 83.4%. The new algorithm\u27s combination of semantic relationships with template mining significantly improves the recall of fpmies. The study\u27s results also show that fpmies is much more efficient than manually trying to extract information. Finally, the performance of the fpmies system was compared with the most popular fpm algorithm, apriori, yielding a significantly improved recall and precision for fpmies (76.7% and 74.6% respectively) compared to that of apriori (30% recall and 24.6% precision)