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Android and Computer Aided Language Learning — Ling575, Winter Qtr. 2011. CALL Benchmarking. Applications. Translation APIs. Mockup. Hello, World. Downloads. Web Services (Français) NLP Systems & Applications: Knowledge Base Population — Ling573, Spring Qtr. 2010. Course description This course examines building coherent systems to handle practical applications.
Particular topics vary. This term we will be focusing on question-answering. Course Resources Textbook There is no required textbook for this course. A number of published research articles will also provide background for the course. Prerequisites: Ling 570, Ling 571, Ling 572 CS 326 (Data Structures) or equivalent Stat 391 (Probability and Statistics for CS) or equivalent Formal grammars, languages, and automata Programming in one or more of Java, Python, C/C++, or Perl Linux/Unix commands Grading 90%: Project Deliverables and Presentations 10%: Class participation Course Mechanics Additional detailed information on grading, collaboration, incompletes, etc.
Tentative schedule, subject to change without notice. Free World Cities Database. YAGO2 - D5: Databases and Information Systems (Max-Planck-Institut für Informatik) Overview YAGO is a huge semantic knowledge base, derived from Wikipedia WordNet and GeoNames. Currently, YAGO 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%.
YAGO is developed jointly with the DBWeb group at Télécom ParisTech University. FUSE: Filesystem in Userspace. Ephyra.info. Lucene - Apache Lucene Core. Apache LuceneTM 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. Please use the links on the right to access Lucene. Lucene offers powerful features through a simple API: Scalable, High-Performance Indexing over 150GB/hour on modern hardwaresmall RAM requirements -- only 1MB heapincremental indexing as fast as batch indexingindex size roughly 20-30% the size of text indexed Powerful, Accurate and Efficient Search Algorithms Cross-Platform Solution Available as Open Source software under the Apache License which lets you use Lucene in both commercial and Open Source programs100%-pure JavaImplementations in other programming languages available that are index-compatible The Apache Software Foundation.
Tagged and Cleaned Wikipedia (TC Wikipedia) and its Ngram. Message Understanding Conference. The 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. Topics and Exercises[edit] 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. For each topic fields were given, which had to be filled with information from the text. At the sixth conference (MUC-6) the task of recognition of named entities and coreference was added.
Literature[edit] Ralph Grishman, Beth Sundheim: Message Understanding Conference - 6: A Brief History. See also[edit] DARPA TIPSTER Program. 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. This program began with a pilot study in 1999. While the ACE program is directed toward extraction of information from audio and image sources in addition to pure text, the research effort is restricted to information extraction from text. The program relates to English, Arabic and Chinese texts. The effort involves: In general objective, the ACE program is motivated by and addresses the same issues as the MUC program that preceded it. References[edit] Text Analysis Conference (TAC) 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 currently hosts evaluations and workshops in two areas of research: Knowledge Base Population (KBP) TAC Workshop: November 17-18, 2014 (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.
Summarization The TAC Summarization track will focus on summarization of scientific literature. Details TBA. 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.
An example is the extraction from news wire reports of corporate mergers, such as denoted by the formal relation: from an online news sentence such as: "Yesterday, New York based Foo Inc. announced their acquisition of Bar Corp. " A broad goal of IE is to allow computation to be done on the previously unstructured data. History[edit] Beginning in 1987, IE was spurred by a series of Message Understanding Conferences. Present significance[edit] Lists. 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". Many universities and public libraries use IR systems to provide access to books, journals and other documents. Web search engines are the most visible IR applications. Overview[edit] An information retrieval process begins when a user enters a query into the system. An object is an entity that is represented by information in a database. Most IR systems compute a numeric score on how well each object in the database matches the query, and rank the objects according to this value. History[edit] Model types[edit] For effectively retrieving relevant documents by IR strategies, the documents are typically transformed into a suitable representation. Recall[edit] Demo Systems — Ling573, Spring Qtr. 2010.
Named Entity Demo. About the Named Entity Demo Named entity recognition finds mentions of things in text. The interface in LingPipe provides character offset representations as chunkings. Genre-Specific Models Named entity recognizers in LingPipe are trained from a corpus of data. Language-Specific Models Although we're only providing English data here, there is training data available (usually for research purposes only) in a number of languages, including Arabic, Chinese, Dutch, German, Greek, Hindi, Japanese, Korean, Portuguese and Spanish.
LingPipe's Recognizers LingPipe provides three statistical named-entity recognizers: Sentence Annotation Included The demos use the appropriate sentence models. Named Entity XML Markup First-best output Entities are marked as in MUC, with an ENAMEX element with attribute TYPE indicating the kind of entity. N-best output Per tag confidence output Each token and its analyses are wrapped with an nBestEntities element. Named Entity Demo on the Web Named Entity Demo via GUI. Entity Extractor SDK Finds People, Places, and Organizations in Text. Big Text represents the vast majority of the world’s big data. Lying hidden within that text is extremely valuable information, unable to be accessed unless read manually—a challenge compounded when foreign languages are involved. This hidden data often comes in the form of entities—names, places, dates, and other words and phrases that establish the real meaning in the text.
Rosette® Entity Extractor (REX) instantly scans through huge volumes of multilingual, unstructured text and tags key data. REX uses multiple approaches to achieve the most accurate results: advanced statistical modeling, customizable rules, and pre-defined lists. As linguistics experts with deep understanding at the intersection of language and technology, Basis Technology continually improves the Rosette product family with language additions, feature updates, and the latest innovations from the academic world.
GitSetup < Main < TWiki. Git repositories allow for many types of workflows, centralized or decentralized. Before creating your repo, decide which steps to follow: Create A Local Repository If you will be working primarily on a local machine, you may simply create a git repo by using cd to change to the directory you wish to place under version control, then typing: git init To initialize a git repo in that directory. From then on, you can run git commands in that directory. Create A Remote Repository If you will be working with your code primarily on patas, you will likely want to create your initial repository there. ssh to patas.ling.washington.edu cd to the directory you wish to place under version control type "git init" in this directory.
Cloning the Remote Repository If you wish to maintain a local copy of your code, you can clone the repository from patas by doing the following: Create a Shared Repository on Patas "Bare" repositories.