The Acquisition Of Lexical Knowledge From The Web For Aspects Of Semantic Interpretation

This work investigates the effective acquisition of lexical knowledge from the Web to perform semantic interpretation. The Web provides an unprecedented amount of natural language from which to gain knowledge useful for semantic interpretation. The knowledge acquired is described as common sense knowledge, information one uses in his or her daily life to understand language and perception. Novel approaches are presented for both the acquisition of this knowledge and use of the knowledge in semantic interpretation algorithms. The goal is to increase accuracy over other automatic semantic interpretation systems, and in turn enable stronger real world applications such as machine translation, advanced Web search, sentiment analysis, and question answering. The major contributions of this dissertation consist of two methods of acquiring lexical knowledge from the Web, namely a database of common sense knowledge and Web selectors. The first method is a framework for acquiring a database of concept relationships. To acquire this knowledge, relationships between nouns are found on the Web and analyzed over WordNet using information-theory, producing information about concepts rather than ambiguous words. For the second contribution, words called Web selectors are retrieved which take the place of an instance of a target word in its local context. The selectors serve for the system to learn the types of concepts that the sense of a target word should be similar. Web selectors are acquired dynamically as part of a semantic interpretation algorithm, while the relationships in the database are useful to iii stand-alone programs. A final contribution of this dissertation concerns a novel semantic similarity measure and an evaluation of similarity and relatedness measures on tasks of concept similarity. Such tasks are useful when applying acquired knowledge to semantic interpretation. Applications to word sense disambiguation, an aspect of semantic interpretation, are used to evaluate the contributions. Disambiguation systems which utilize semantically annotated training data are considered supervised. The algorithms of this dissertation are considered minimallysupervised; they do not require training data created by humans, though they may use humancreated data sources. In the case of evaluating a database of common sense knowledge, integrating the knowledge into an existing minimally-supervised disambiguation system significantly improved results – a 20.5% error reduction. Similarly, the Web selectors disambiguation system, which acquires knowledge directly as part of the algorithm, achieved results comparable with top minimally-supervised systems, an F-score of 80.2% on a standard noun disambiguation task. This work enables the study of many subsequent related tasks for improving semantic interpretation and its application to real-world technologies. Other aspects of semantic interpretation, such as semantic role labeling could utilize the same methods presented here for word sense disambiguation. As the Web continues to grow, the capabilities of the systems in this dissertation are expected to increase. Although the Web selectors system achieves great results, a study in this dissertation shows likely improvements from acquiring more data. Furthermore, the methods for acquiring a database of common sense knowledge could be applied in a more exhaustive fashion for other types of common sense knowledge. Finally, perhaps the greatest benefits from this work will come from the enabling of real world technologies that utilize semantic interpretation

Commonsense knowledge acquisition and applications

Computers are increasingly expected to make smart decisions based on what humans consider commonsense. This would require computers to understand their environment, including properties of objects in the environment (e.g., a wheel is round), relations between objects (e.g., two wheels are part of a bike, or a bike is slower than a car) and interactions of objects (e.g., a driver drives a car on the road). The goal of this dissertation is to investigate automated methods for acquisition of large-scale, semantically organized commonsense knowledge. Prior state-of-the-art methods to acquire commonsense are either not automated or based on shallow representations. Thus, they cannot produce large-scale, semantically organized commonsense knowledge. To achieve the goal, we divide the problem space into three research directions, constituting our core contributions: 1. Properties of objects: acquisition of properties like hasSize, hasShape, etc. We develop WebChild, a semi-supervised method to compile semantically organized properties. 2. Relationships between objects: acquisition of relations like largerThan, partOf, memberOf, etc. We develop CMPKB, a linear-programming based method to compile comparative relations, and, we develop PWKB, a method based on statistical and logical inference to compile part-whole relations. 3. Interactions between objects: acquisition of activities like drive a car, park a car, etc., with attributes such as temporal or spatial attributes. We develop Knowlywood, a method based on semantic parsing and probabilistic graphical models to compile activity knowledge. Together, these methods result in the construction of a large, clean and semantically organized Commonsense Knowledge Base that we call WebChild KB.Von Computern wird immer mehr erwartet, dass sie kluge Entscheidungen treffen können, basierend auf Allgemeinwissen. Dies setzt voraus, dass Computer ihre Umgebung, einschließlich der Eigenschaften von Objekten (z. B. das Rad ist rund), Beziehungen zwischen Objekten (z. B. ein Fahrrad hat zwei Räder, ein Fahrrad ist langsamer als ein Auto) und Interaktionen von Objekten (z. B. ein Fahrer fährt ein Auto auf der Straße), verstehen können. Das Ziel dieser Dissertation ist es, automatische Methoden für die Erfassung von großmaßstäblichem, semantisch organisiertem Allgemeinwissen zu schaffen. Dies ist schwierig aufgrund folgender Eigenschaften des Allgemeinwissens. Es ist: (i) implizit und spärlich, da Menschen nicht explizit das Offensichtliche ausdrücken, (ii) multimodal, da es über textuelle und visuelle Inhalte verteilt ist, (iii) beeinträchtigt vom Einfluss des Berichtenden, da ungewöhnliche Fakten disproportional häufig berichtet werden, (iv) Kontextabhängig, und hat aus diesem Grund eine eingeschränkte statistische Konfidenz. Vorherige Methoden, auf diesem Gebiet sind entweder nicht automatisiert oder basieren auf flachen Repräsentationen. Daher können sie kein großmaßstäbliches, semantisch organisiertes Allgemeinwissen erzeugen. Um unser Ziel zu erreichen, teilen wir den Problemraum in drei Forschungsrichtungen, welche den Hauptbeitrag dieser Dissertation formen: 1. Eigenschaften von Objekten: Erfassung von Eigenschaften wie hasSize, hasShape, usw. Wir entwickeln WebChild, eine halbüberwachte Methode zum Erfassen semantisch organisierter Eigenschaften. 2. Beziehungen zwischen Objekten: Erfassung von Beziehungen wie largerThan, partOf, memberOf, usw. Wir entwickeln CMPKB, eine Methode basierend auf linearer Programmierung um vergleichbare Beziehungen zu erfassen. Weiterhin entwickeln wir PWKB, eine Methode basierend auf statistischer und logischer Inferenz welche zugehörigkeits Beziehungen erfasst. 3. Interaktionen zwischen Objekten: Erfassung von Aktivitäten, wie drive a car, park a car, usw. mit temporalen und räumlichen Attributen. Wir entwickeln Knowlywood, eine Methode basierend auf semantischem Parsen und probabilistischen grafischen Modellen um Aktivitätswissen zu erfassen. Als Resultat dieser Methoden erstellen wir eine große, saubere und semantisch organisierte Allgemeinwissensbasis, welche wir WebChild KB nennen

Term-driven E-Commerce

Die Arbeit nimmt sich der textuellen Dimension des E-Commerce an. Grundlegende Hypothese ist die textuelle Gebundenheit von Information und Transaktion im Bereich des elektronischen Handels. Überall dort, wo Produkte und Dienstleistungen angeboten, nachgefragt, wahrgenommen und bewertet werden, kommen natürlichsprachige Ausdrücke zum Einsatz. Daraus resultiert ist zum einen, wie bedeutsam es ist, die Varianz textueller Beschreibungen im E-Commerce zu erfassen, zum anderen können die umfangreichen textuellen Ressourcen, die bei E-Commerce-Interaktionen anfallen, im Hinblick auf ein besseres Verständnis natürlicher Sprache herangezogen werden

Commonsense knowledge acquisition and applications

Computers are increasingly expected to make smart decisions based on what humans consider commonsense. This would require computers to understand their environment, including properties of objects in the environment (e.g., a wheel is round), relations between objects (e.g., two wheels are part of a bike, or a bike is slower than a car) and interactions of objects (e.g., a driver drives a car on the road). The goal of this dissertation is to investigate automated methods for acquisition of large-scale, semantically organized commonsense knowledge. Prior state-of-the-art methods to acquire commonsense are either not automated or based on shallow representations. Thus, they cannot produce large-scale, semantically organized commonsense knowledge. To achieve the goal, we divide the problem space into three research directions, constituting our core contributions: 1. Properties of objects: acquisition of properties like hasSize, hasShape, etc. We develop WebChild, a semi-supervised method to compile semantically organized properties. 2. Relationships between objects: acquisition of relations like largerThan, partOf, memberOf, etc. We develop CMPKB, a linear-programming based method to compile comparative relations, and, we develop PWKB, a method based on statistical and logical inference to compile part-whole relations. 3. Interactions between objects: acquisition of activities like drive a car, park a car, etc., with attributes such as temporal or spatial attributes. We develop Knowlywood, a method based on semantic parsing and probabilistic graphical models to compile activity knowledge. Together, these methods result in the construction of a large, clean and semantically organized Commonsense Knowledge Base that we call WebChild KB.Von Computern wird immer mehr erwartet, dass sie kluge Entscheidungen treffen können, basierend auf Allgemeinwissen. Dies setzt voraus, dass Computer ihre Umgebung, einschließlich der Eigenschaften von Objekten (z. B. das Rad ist rund), Beziehungen zwischen Objekten (z. B. ein Fahrrad hat zwei Räder, ein Fahrrad ist langsamer als ein Auto) und Interaktionen von Objekten (z. B. ein Fahrer fährt ein Auto auf der Straße), verstehen können. Das Ziel dieser Dissertation ist es, automatische Methoden für die Erfassung von großmaßstäblichem, semantisch organisiertem Allgemeinwissen zu schaffen. Dies ist schwierig aufgrund folgender Eigenschaften des Allgemeinwissens. Es ist: (i) implizit und spärlich, da Menschen nicht explizit das Offensichtliche ausdrücken, (ii) multimodal, da es über textuelle und visuelle Inhalte verteilt ist, (iii) beeinträchtigt vom Einfluss des Berichtenden, da ungewöhnliche Fakten disproportional häufig berichtet werden, (iv) Kontextabhängig, und hat aus diesem Grund eine eingeschränkte statistische Konfidenz. Vorherige Methoden, auf diesem Gebiet sind entweder nicht automatisiert oder basieren auf flachen Repräsentationen. Daher können sie kein großmaßstäbliches, semantisch organisiertes Allgemeinwissen erzeugen. Um unser Ziel zu erreichen, teilen wir den Problemraum in drei Forschungsrichtungen, welche den Hauptbeitrag dieser Dissertation formen: 1. Eigenschaften von Objekten: Erfassung von Eigenschaften wie hasSize, hasShape, usw. Wir entwickeln WebChild, eine halbüberwachte Methode zum Erfassen semantisch organisierter Eigenschaften. 2. Beziehungen zwischen Objekten: Erfassung von Beziehungen wie largerThan, partOf, memberOf, usw. Wir entwickeln CMPKB, eine Methode basierend auf linearer Programmierung um vergleichbare Beziehungen zu erfassen. Weiterhin entwickeln wir PWKB, eine Methode basierend auf statistischer und logischer Inferenz welche zugehörigkeits Beziehungen erfasst. 3. Interaktionen zwischen Objekten: Erfassung von Aktivitäten, wie drive a car, park a car, usw. mit temporalen und räumlichen Attributen. Wir entwickeln Knowlywood, eine Methode basierend auf semantischem Parsen und probabilistischen grafischen Modellen um Aktivitätswissen zu erfassen. Als Resultat dieser Methoden erstellen wir eine große, saubere und semantisch organisierte Allgemeinwissensbasis, welche wir WebChild KB nennen

Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach

CLiFF Notes: Research in the Language Information and Computation Laboratory of The University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLIFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science, Psychology, and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. With 48 individual contributors and six projects represented, this is the largest LINC Lab collection to date, and the most diverse

Mining photographic collections to enhance the precision and recall of search results using semantically controlled query expansion

Driven by a larger and more diverse user-base and datasets, modern Information Retrieval techniques are striving to become contextually-aware in order to provide users with a more satisfactory search experience. While text-only retrieval methods are significantly more accurate and faster to render results than purely visual retrieval methods, these latter provide a rich complementary medium which can be used to obtain relevant and different results from those obtained using text-only retrieval. Moreover, the visual retrieval methods can be used to learn the user’s context and preferences, in particular the user’s relevance feedback, and exploit them to narrow down the search to more accurate results. Despite the overall deficiency in precision of visual retrieval result, the top results are accurate enough to be used for query expansion, when expanded in a controlled manner. The method we propose overcomes the usual pitfalls of visual retrieval: 1. The hardware barrier giving rise to prohibitively slow systems. 2. Results dominated by noise. 3. A significant gap between the low-level features and the semantics of the query. In our thesis, the first barrier is overcome by employing a simple block-based visual features which outperforms a method based on MPEG-7 features specially at early precision (precision of the top results). For the second obstacle, lists from words semantically weighted according to their degree of relation to the original query or to relevance feedback from example images are formed. These lists provide filters through which the confidence in the candidate results is assessed for inclusion in the results. This allows for more reliable Pseudo-Relevance Feedback (PRF). This technique is then used to bridge the third barrier; the semantic gap. It consists of a second step query, re-querying the data set with an query expanded with weighted words obtained from the initial query, and semantically filtered (SF) without human intervention. We developed our PRF-SF method on the IAPR TC-12 benchmark dataset of 20,000 tourist images, obtaining promising results, and tested it on the different and much larger Belga benchmark dataset of approximately 500,000 news images originating from a different source. Our experiments confirmed the potential of the method in improving the overall Mean Average Precision, recall, as well as the level of diversity of the results measured using cluster recall

CLiFF Notes: Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

One concern of the Computer Graphics Research Lab is in simulating human task behavior and understanding why the visualization of the appearance, capabilities and performance of humans is so challenging. Our research has produced a system, called Jack, for the definition, manipulation, animation and human factors analysis of simulated human figures. Jack permits the envisionment of human motion by interactive specification and simultaneous execution of multiple constraints, and is sensitive to such issues as body shape and size, linkage, and plausible motions. Enhanced control is provided by natural behaviors such as looking, reaching, balancing, lifting, stepping, walking, grasping, and so on. Although intended for highly interactive applications, Jack is a foundation for other research. The very ubiquitousness of other people in our lives poses a tantalizing challenge to the computational modeler: people are at once the most common object around us, and yet the most structurally complex. Their everyday movements are amazingly fluid, yet demanding to reproduce, with actions driven not just mechanically by muscles and bones but also cognitively by beliefs and intentions. Our motor systems manage to learn how to make us move without leaving us the burden or pleasure of knowing how we did it. Likewise we learn how to describe the actions and behaviors of others without consciously struggling with the processes of perception, recognition, and language. Present technology lets us approach human appearance and motion through computer graphics modeling and three dimensional animation, but there is considerable distance to go before purely synthesized figures trick our senses. We seek to build computational models of human like figures which manifest animacy and convincing behavior. Towards this end, we: Create an interactive computer graphics human model; Endow it with reasonable biomechanical properties; Provide it with human like behaviors; Use this simulated figure as an agent to effect changes in its world; Describe and guide its tasks through natural language instructions. There are presently no perfect solutions to any of these problems; ultimately, however, we should be able to give our surrogate human directions that, in conjunction with suitable symbolic reasoning processes, make it appear to behave in a natural, appropriate, and intelligent fashion. Compromises will be essential, due to limits in computation, throughput of display hardware, and demands of real-time interaction, but our algorithms aim to balance the physical device constraints with carefully crafted models, general solutions, and thoughtful organization. The Jack software is built on Silicon Graphics Iris 4D workstations because those systems have 3-D graphics features that greatly aid the process of interacting with highly articulated figures such as the human body. Of course, graphics capabilities themselves do not make a usable system. Our research has therefore focused on software to make the manipulation of a simulated human figure easy for a rather specific user population: human factors design engineers or ergonomics analysts involved in visualizing and assessing human motor performance, fit, reach, view, and other physical tasks in a workplace environment. The software also happens to be quite usable by others, including graduate students and animators. The point, however, is that program design has tried to take into account a wide variety of physical problem oriented tasks, rather than just offer a computer graphics and animation tool for the already computer sophisticated or skilled animator. As an alternative to interactive specification, a simulation system allows a convenient temporal and spatial parallel programming language for behaviors. The Graphics Lab is working with the Natural Language Group to explore the possibility of using natural language instructions, such as those found in assembly or maintenance manuals, to drive the behavior of our animated human agents. (See the CLiFF note entry for the AnimNL group for details.) Even though Jack is under continual development, it has nonetheless already proved to be a substantial computational tool in analyzing human abilities in physical workplaces. It is being applied to actual problems involving space vehicle inhabitants, helicopter pilots, maintenance technicians, foot soldiers, and tractor drivers. This broad range of applications is precisely the target we intended to reach. The general capabilities embedded in Jack attempt to mirror certain aspects of human performance, rather than the specific requirements of the corresponding workplace. We view the Jack system as the basis of a virtual animated agent that can carry out tasks and instructions in a simulated 3D environment. While we have not yet fooled anyone into believing that the Jack figure is real , its behaviors are becoming more reasonable and its repertoire of actions more extensive. When interactive control becomes more labor intensive than natural language instructional control, we will have reached a significant milestone toward an intelligent agent