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LING 575 Voice
Android and Computer Aided Language Learning — Ling575, Winter Qtr. 2011
Course description The course will cover the theory and practice of spoken dialog systems. The course will have readings and lectures on general techniques and issues in spoken dialog systems, and will use publicly available tools and toolkits to investigate spoken dialog systems. The target will be conversational systems that are more flexible than the typical flight status phone system.CALL Benchmarking
Applications
Translation APIs
Mockup
Hello, World
Welcome to Android application development! This class teaches you how to build your first Android app. You’ll learn how to create an Android project and run a debuggable version of the app. You'll also learn some fundamentals of Android app design, including how to build a simple user interface and handle user input. Before you start this class, be sure you have your development environment set up. You need to:
Course description This course examines building coherent systems to handle practical applications. Particular topics vary. This term we will be focussing on question-answering. Textbook
NLP Systems & Applications: Knowledge Base Population — Ling573, Spring Qtr. 2010
Overview YAGO2s is a huge semantic knowledge base, derived from Wikipedia WordNet and GeoNames . Currently, YAGO2s has knowledge of more than 10 million entities (like persons, organizations, cities, etc.) and contains more than 120 million facts about these entities. YAGO is special in several ways: The accuracy of YAGO has been manually evaluated, proving a confirmed accuracy of 95%. Every relation is annotated with its confidence value.
YAGO2 - D5: Databases and Information Systems (Max-Planck-Institut für Informatik)
Lucene - Apache Lucene Core
Apache Lucene TM is a high-performance, full-featured text search engine library written entirely in Java. It is a technology suitable for nearly any application that requires full-text search, especially cross-platform. Apache Lucene is an open source project available for free download.
wiki.dbpedia.org : About
DBpedia is a crowd-sourced community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to make sophisticated queries against Wikipedia, and to link other data sets on the Web to Wikipedia data. We hope this will make it easier for the amazing amount of information in Wikipedia to be used in new and interesting ways, and that it might inspire new mechanisms for navigating, linking, and improving the encyclopedia itself. NewsThe Message Understanding Conferences (MUC) were initiated and financed by DARPA (Defense Advanced Research Projects Agency) to encourage the development of new and better methods of information extraction .The character of this competition—many concurrent research teams competing against one another—required the development of standards for evaluation, e.g. the adoption of metrics like precision and recall . [ edit ] Topics and Exercises Only for the first conference (MUC-1) could the participant choose the output format for the extracted information. From the second conference the output format, by which the participants' systems would be evaluated, was prescribed.
Message Understanding Conference
Automatic Content Extraction
Automatic Content Extraction (ACE) is a program for developing advanced Information extraction technologies. Given a text in natural language, the ACE challenge is to detect: entities mentioned in the text, such as: persons, organizations, locations, facilities, weapons, vehicles, and geo-political entities. relations between entities, such as: person A is the manager of company B. Relation types include: role, part, located, near, and social. events mentioned in the text, such as: interaction, movement, transfer, creation and destruction.The Text Analysis Conference (TAC) is a series of evaluation workshops organized to encourage research in Natural Language Processing and related applications, by providing a large test collection, common evaluation procedures, and a forum for organizations to share their results. TAC comprises sets of tasks known as "tracks," each of which focuses on a particular subproblem of NLP. TAC tracks focus on end-user tasks, but also include component evaluations situated within the context of end-user tasks. TAC 2013 hosts evaluations and workshops in two areas of research: Knowledge Base Population (KBP) TAC KBP Workshop: November 18-19, 2013 (Gaithersburg, MD, USA) The goal of Knowledge Base Population is to promote research in automated systems that discover information about named entities as found in a large corpus and incorporate this information into a knowledge base.
Text Analysis Conference (TAC)
Information extraction
Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP). Recent activities in multimedia document processing like automatic annotation and content extraction out of images/audio/video could be seen as information extraction. Due to the difficulty of the problem, current approaches to IE focus on narrowly restricted domains.Information retrieval
Information retrieval is the activity of obtaining information resources relevant to an information need from a collection of information resources. Searches can be based on metadata or on full-text (or other content-based) indexing. Automated information retrieval systems are used to reduce what has been called " information overload ".About the Named Entity Demo Named entity recognition finds mentions of things in text. The interface in LingPipe provides character offset representations as chunkings.
LingPipe: Named Entity Demo
Identify Names, Places, Organizations, and Other Entities in Your Text Rosette® Entity Extractor turns raw data into concepts. This named entity recognition software provides semantic tagging to find entities in text. It builds this metadata by analyzing the text with a hybrid model built from a deep, statistical analysis of the language and a collection of rules about which words represent entities. Used by Leading Search Engines and Intelligence Agencies Basis Technology has years of experience providing software tools for analyzing and extracting information from multilingual text.