Statistical approaches to processing natural language text have become dominant in recent years This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear The book contains all the theory and algorithms needed for building NLP tools It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications

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Foundations of Statistical Natural Language Processing

We describe a method for the automatic acquisition of the hyponymy lexical relation from unrestricted text. Two goals motivate the approach: (i) avoidance of the need for pre-encoded knowledge and (ii) applicability across a wide range of text. We identify a set of lexico-syntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest. We describe a method for discovering these patterns and suggest that other lexical relations will also be acquirable in this way. A subset of the acquisition algorithm is implemented and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications to areas such as information retrieval are suggested.

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Automatic acquisition of hyponyms from large text corpora

The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes it freely available on the Web using Semantic Web and Linked Data technologies. The project extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things. The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. The DBpedia project maps Wikipedia infoboxes from 27 different language editions to a single shared ontology consisting of 320 classes and 1,650 properties. The mappings are created via a world-wide crowd-sourcing effort and enable knowledge from the different Wikipedia editions to be combined. The project publishes releases of all DBpedia knowledge bases for download and provides SPARQL query access to 14 out of the 111 language editions via a global network of local DBpedia chapters. In addition to the regular releases, the project maintains a live knowledge base which is updated whenever a page in Wikipedia changes. DBpedia sets 27 million RDF links pointing into over 30 external data sources and thus enables data from these sources to be used together with DBpedia data. Several hundred data sets on the Web publish RDF links pointing to DBpedia themselves and make DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud. In this system report, we give an overview of the DBpedia community project, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications.

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DBpedia - A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia

1. Logic. 2. Ontology. 3. Knowledge Representation. 4. Processes. 5. Purposes, Contexts, And Agents. 6. Knowledge Soup. 7. Knowledge Acquisition And Sharing. Appendixes: Appendix A: Summary Of Notations Appendix B: Sample Ontology. Appendix C: Extended Example. Answers To Selected Exercises. Bibliography. Name Index. Subject Index. Special Symbols.

Knowledge Representation: Logical, Philosophical, and Computational Foundations

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval/Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed.

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Word sense disambiguation: A survey

Born four years ago as a Semantic Web extension for the web browser Firefox, Semantic Turkey pushed forward the traditional concept of links&folders-based bookmarking to a new dimension, allowing users to keep track of relevant information from visited web sites and to organize the collected content according to standard or personally defined ontologies. Today, the tool has broken the boundaries of its original intents and can be considered, under every aspect, an extensible platform for knowledge management and acquisition. The semantic bookmarking and annotation facilities of Semantic Turkey are now supporting just a part of a whole methodology where different actors, from domain experts to knowledge engineers, can cooperate in developing, building and populating ontologies while navigating the Web.

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Semantic turkey: a browser-integrated environment for knowledge acquisition and management

Are linguistic properties and behaviors important to recognize terms? Are statistical measures effective to extract terms? Is it possible to capture a sort of termhood with computation linguistic techniques? Or maybe, terms are too much sensitive to exogenous and pragmatic factors that cannot be confined in computational linguistic? All these questions are still open. This study tries to contribute in the search of an answer, with the belief that it can be found only through a careful experimental analysis of real case studies and a study of their correlation with theoretical insights.

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Terminology Extraction: An Analysis of Linguistic and Statistical Approaches

The paper reports on the design and construction of a multi-layered corpus of Italian, annotated at the syntactic and lexico-semantic levels, whose development is supported by dedicated software augmented with an intelligent interface. The issue of evaluating this type of resource is also addressed.

Building the Italian Syntactic-Semantic Treebank

Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology

Natural language processing will not be able to compete with traditional information retrieval unless high-coverage techniques are developed. It is commonly agreed that a poor encoding of the semantic lexicon is the bottleneck of many existing systems. A hand encoding of semantic knowledge on an extensive basis is not realistic; hence, it is important to devise methods by which such knowledge can be acquired in part or entirely by a computer. But what type of semantic knowledge could be automatically learned, from which sources, and by what methods? This paper explores the above issues and proposes an algorithm to learn syncategorematic concepts from text exemplars. What is learned about a concept is not its defining features, such as kinship, but rather its patterns of use.The knowledge acquisition method is based on learning by observations; observations are examples of word co-occurrences (collocations) in a large corpus, detected by a morphosyntactic analyzer. A semantic bias is used to associate collocations with the appropriate meaning relation, if one exists. Based upon single or multiple examples, the acquired knowledge is then generalized to create semantic rules on concept uses.Interactive human intervention is required in the training phase, when the bias is defined and refined. The duration of this phase depends upon the semantic closure of the sublanguage on which the experiment is carried out. After training, final approval by a linguist is still needed for the acquired semantic rules. At the current stage of experimentation of this system, it is unclear whether and when human supervision could be further reduced.

Maria Teresa Pazienza

Papers

Semantic turkey: a browser-integrated environment for knowledge acquisition and management

Terminology Extraction: An Analysis of Linguistic and Statistical Approaches

Building the Italian Syntactic-Semantic Treebank

Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology

How to encode semantic knowledge: a method for meaning representation and computer-aided acquisition