A General FOFE-net Framework for Simple and Effective Question Answering over Knowledge Bases

Question answering over knowledge base (KB-QA) has recently become a popular research topic in NLP. One of the popular ways to solve the KBQA problem is to make use of a pipeline of several NLP modules, including entity discovery and linking (EDL) and relation detection. Recent success on KBQA task usually involves complex network structures with sophisticated heuristics. Inspired by a previous work that builds a strong KBQA baseline, we propose a simple but general neural model composed of fixed-size ordinally forgetting encoding (FOFE) and deep neural networks, called FOFE-net to solve KB-QA problem at different stages. For evaluation, we use two popular KB-QA datasets, SimpleQuestions, WebQSP, and our newly created dataset, FreebaseQA. The experimental results show that FOFE-net performs well on KBQA subtasks, entity discovery and linking (EDL) and relation detection, and in turn pushing overall KB-QA system to achieve strong results on all the datasets

Entities with quantities : extraction, search, and ranking

Quantities are more than numeric values. They denote measures of the world’s entities such as heights of buildings, running times of athletes, energy efficiency of car models or energy production of power plants, all expressed in numbers with associated units. Entity-centric search and question answering (QA) are well supported by modern search engines. However, they do not work well when the queries involve quantity filters, such as searching for athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. State-of-the-art systems fail to understand the quantities, including the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.), and the context of the quantity (200m race, quarterly revenue, etc.). QA systems based on structured knowledge bases (KBs) also fail as quantities are poorly covered by state-of-the-art KBs. In this dissertation, we developed new methods to advance the state-of-the-art on quantity knowledge extraction and search.Zahlen sind mehr als nur numerische Werte. Sie beschreiben Maße von Entitäten wie die Höhe von Gebäuden, die Laufzeit von Sportlern, die Energieeffizienz von Automodellen oder die Energieerzeugung von Kraftwerken - jeweils ausgedrückt durch Zahlen mit zugehörigen Einheiten. Entitätszentriete Anfragen und direktes Question-Answering werden von Suchmaschinen häufig gut unterstützt. Sie funktionieren jedoch nicht gut, wenn die Fragen Zahlenfilter beinhalten, wie z. B. die Suche nach Sportlern, die 200m unter 20 Sekunden gelaufen sind, oder nach Unternehmen mit einem Quartalsumsatz von über 2 Milliarden US-Dollar. Selbst moderne Systeme schaffen es nicht, Quantitäten, einschließlich der genannten Bedingungen (weniger als, über, etc.), der Maßeinheiten (Sekunden, Dollar, etc.) und des Kontexts (200-Meter-Rennen, Quartalsumsatz usw.), zu verstehen. Auch QA-Systeme, die auf strukturierten Wissensbanken (“Knowledge Bases”, KBs) aufgebaut sind, versagen, da quantitative Eigenschaften von modernen KBs kaum erfasst werden. In dieser Dissertation werden neue Methoden entwickelt, um den Stand der Technik zur Wissensextraktion und -suche von Quantitäten voranzutreiben. Unsere Hauptbeiträge sind die folgenden: • Zunächst präsentieren wir Qsearch [Ho et al., 2019, Ho et al., 2020] – ein System, das mit erweiterten Fragen mit Quantitätsfiltern umgehen kann, indem es Hinweise verwendet, die sowohl in der Frage als auch in den Textquellen vorhanden sind. Qsearch umfasst zwei Hauptbeiträge. Der erste Beitrag ist ein tiefes neuronales Netzwerkmodell, das für die Extraktion quantitätszentrierter Tupel aus Textquellen entwickelt wurde. Der zweite Beitrag ist ein neuartiges Query-Matching-Modell zum Finden und zur Reihung passender Tupel. • Zweitens, um beim Vorgang heterogene Tabellen einzubinden, stellen wir QuTE [Ho et al., 2021a, Ho et al., 2021b] vor – ein System zum Extrahieren von Quantitätsinformationen aus Webquellen, insbesondere Ad-hoc Webtabellen in HTML-Seiten. Der Beitrag von QuTE umfasst eine Methode zur Verknüpfung von Quantitäts- und Entitätsspalten, für die externe Textquellen genutzt werden. Zur Beantwortung von Fragen kontextualisieren wir die extrahierten Entitäts-Quantitäts-Paare mit informativen Hinweisen aus der Tabelle und stellen eine neue Methode zur Konsolidierung und verbesserteer Reihung von Antwortkandidaten durch Inter-Fakten-Konsistenz vor. • Drittens stellen wir QL [Ho et al., 2022] vor – eine Recall-orientierte Methode zur Anreicherung von Knowledge Bases (KBs) mit quantitativen Fakten. Moderne KBs wie Wikidata oder YAGO decken viele Entitäten und ihre relevanten Informationen ab, übersehen aber oft wichtige quantitative Eigenschaften. QL ist frage-gesteuert und basiert auf iterativem Lernen mit zwei Hauptbeiträgen, um die KB-Abdeckung zu verbessern. Der erste Beitrag ist eine Methode zur Expansion von Fragen, um einen größeren Pool an Faktenkandidaten zu erfassen. Der zweite Beitrag ist eine Technik zur Selbstkonsistenz durch Berücksichtigung der Werteverteilungen von Quantitäten

Winona Daily News

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The "About to teach" course: an introductory orientation course for secondary teachers in training: an evaluation of student assessments

This piece of research is an attempt to evaluate the assessments made by secondary teachers in training of an introductory orientation course offered during the first seven weeks of the 1984 H.D. E. course in the Department of Education of Rhodes University. This course, the About To Teach (ATT) course, was introduced in an attempt to obviate some of the perceived problems that students experience in the initial months of their H.D.E. year. The course was first offered in 1982 and in both 1982 and 1983 it was assessed by the students. The evaluation of the assessments offered in those two years provided much of the background for this in-depth look at student assessments of the 1984 ATT course. Briefly, the course attempts to offer the students a stimulating, meaningful, interesting and enjoyable learning experience which will help them to orientate; prepare them adequately for their first teaching practice and the reception later of the offerings of the core theory discipline of Philosophy, Sociology and Psychology. The course itself is a piece of action research and its underlying assumptions are essentially humanistic in nature. Its planners have attempted to bracket as many assumptions as possible and to espouse only those assumptions which are basically positive in nature. It does not attempt to prescribe or offer any dogma which can or must be assessed in any formal sense; it attempts to meet the students from whatever stages in their development they are at when they arrive to commence their H.D.E. year; and it does not attempt to compel the students in any way whatsoever. It is a course which must stand or fall on its own merits. Since the researcher is himself an involved participant in the process, he felt that the completion of a detailed questionnaire and interviews with a sample of the students would be the most economical and the best means of obtaining data for as objective an analysis as possible. To further obviate the possibility of researcher bias all the responses collected have been included in the appendices so that the reader may satisfy him/herself that the interpretations made and conclusions drawn are reasonable. Briefly, the chief conclusion of this researcher is that the overwhelming majority of the students perceived the course as offering them a meaningful learning experience. In addition, it can be argued that the course is, in effect, a guidance course in that it appears to be preparing students for experiences which they still have to come across . Most are generally critical of other courses offered during the H.D . E. year and many make an appeal for, or suggest, a much more integrated approach along the lines of the ATT course . There is a definite appeal for a coherent H.D.E. experience which is meaningful and 'peoplecentred'. By no stretch of the imagination can the findings of this particular piece of research be generalised to any other context since it is very definitely specific in both context and setting. However the researcher is quietly confident that his conclusions and recommendations make a great deal of sense within the specific context of this study