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 <channel rdf:about="http://ebiquity.umbc.edu/tag/natural language processing/">
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    <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/95/Graph-of-Relations"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/521/Integrating-Linked-Open-Data-with-Unstructured-Text-for-Intelligence-Gathering-Tasks"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/492/Entity-Disambiguation-for-Knowledge-Base-Population"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/499/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data"/>
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    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/476/Annotating-named-entities-in-Twitter-data-with-crowdsourcing"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/475/Unsupervised-techniques-for-discovering-ontology-elements-from-Wikipedia-article-links"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/478/Automatic-Discovery-of-Semantic-Relations-using-MindNet"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/461/Ensembles-in-Adversarial-Classification-for-Spam"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/482/HLTCOE-Approaches-to-Knowledge-Base-Population-at-TAC-2009"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/466/Finding-Semantic-Web-Ontology-Terms-from-Words"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/432/Cross-Document-Coreference-Resolution-A-Key-Technology-for-Learning-by-Reading"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/431/Using-Wikitology-for-Cross-Document-Entity-Coreference-Resolution"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/423/Knowledge-Base-Evaluation-for-Semantic-Knowledge-Discovery"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/428/An-Investigation-of-Linguistic-Information-for-Speech-Recognition-Error-Detection"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/388/OWL-as-a-Target-for-Information-Extraction-Systems-"/>
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    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/328/Using-a-Natural-Language-Understanding-System-to-Generate-Semantic-Web-Content"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/301/SemNews-A-Semantic-News-Framework"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/260/Text-understanding-agents-and-the-Semantic-Web"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/261/Integrating-Language-Understanding-Agents-Into-the-Semantic-Web"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/114/The-Integrality-of-Speech-in-Multimodal-Interfaces"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/337/The-Kernel-Natural-Language-Processing-System"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/560/The-Need-for-User-Models-in-Generating-Expert-System-Explanations"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/287/Modeling-the-user-in-natural-language-systems"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/347/Natural-language-interactions-with-artificial-experts"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/285/The-Semantic-Interpretation-of-Nominal-Compounds"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/486/The-Semantic-Interpretation-of-Compound-Nominals"/>
    <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/569/JETS-Achieving-Completeness-through-Coverage-and-Closure"/>
    <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift"/>
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    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/369/Machine-aided-human-translation-An-automated-system-for-transcribing-dictated-document-translations"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/349/Learning-by-Reading-Automatic-Knowledge-Extraction-Through-Semantic-Analysis-"/>
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    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/282/When-is-a-Translation-not-a-Translation-"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/256/Information-Extraction-via-Automatic-Generation-of-Semantic-Classifiers"/>
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 <image rdf:about="http://ebiquity.umbc.edu/img/logo.jpg">
  <title>UMBC ebiquity research group</title>
  <link>http://ebiquity.umbc.edu</link>
  <url>http://ebiquity.umbc.edu/img/logo.jpg</url>
 </image>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/95/Graph-of-Relations">
  <title><![CDATA[Graph of Relations]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/95/Graph-of-Relations</link>
  <description><![CDATA[ 
Users need better ways to explore linked open data collections and obtain information from it. Using SPARQL requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology used by the DBpedia, and URIs for entities of interest.  Natural language question answering systems solve the problem, but these are still subjects of research. We are developing a compromise approach in which non-experts specify a graphical ``skeleton'' for a query and anno...]]></description>
  <dc:date>2010-01-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/521/Integrating-Linked-Open-Data-with-Unstructured-Text-for-Intelligence-Gathering-Tasks">
  <title><![CDATA[Integrating Linked Open Data with Unstructured Text for Intelligence Gathering Tasks]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/521/Integrating-Linked-Open-Data-with-Unstructured-Text-for-Intelligence-Gathering-Tasks</link>
  <description><![CDATA[We present techniques for uncovering links between terror
incidents, organizations, and people involved with these incidents.
Our methods involve performing shallow NLP tasks
to extract entities of interest from documents and using linguistic
pattern matching and filtering techniques to assign
specific relations to the entities discovered. We also gather
more information about these entities from the Linked Open
Data Cloud, and further allow human analysts to add intelligent
inference...]]></description>
  <dc:date>2011-03-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/492/Entity-Disambiguation-for-Knowledge-Base-Population">
  <title><![CDATA[Entity Disambiguation for Knowledge Base Population]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/492/Entity-Disambiguation-for-Knowledge-Base-Population</link>
  <description><![CDATA[The integration of facts derived from information extraction systems into existing knowledge bases requires a system to disambiguate entity mentions in the text. This is challenging due to issues such as non-uniform variations in entity names, mention ambiguity, and entities absent from a knowledge base. We present a state of the art system for entity disambiguation that not only addresses these challenges but also scales to knowledge bases with several million entries using very little resou...]]></description>
  <dc:date>2010-08-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/499/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data">
  <title><![CDATA[Improving Accuracy of Named Entity Recognition on Social Media Data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/499/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data</link>
  <description><![CDATA[In recent years, social media outlets such as Twitter and Facebook have drawn attention from companies and researchers interested in detecting trends. The informal nature of status updates from these services leads to a higher volume of updates, because each update takes little care to generate, but each update is usually short and noisy (misspellings, lack of punctuation, non-standard abbreviations and capitalization). These shortcomings cause traditional Natural Language Processing (NLP) te...]]></description>
  <dc:date>2010-08-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/477/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-Based-on-Linguistic-Analysis-and-Semantic-Typing">
  <title><![CDATA[A Hybrid Approach to Unsupervised Relation Discovery Based on Linguistic Analysis and Semantic Typing]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/477/A-Hybrid-Approach-to-Unsupervised-Relation-Discovery-Based-on-Linguistic-Analysis-and-Semantic-Typing</link>
  <description><![CDATA[This paper describes a hybrid approach for unsupervised and unrestricted relation discovery between entities using output from linguistic analysis and semantic typing information from a knowledge base. We use Factz (encoded as subject, predicate and object triples) produced by Powerset as a result of linguistic analysis. A particular relation may be expressed in a variety of ways in text and hence have multiple facts associated with it. We present an unsupervised approach for collapsing multi...]]></description>
  <dc:date>2010-06-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/476/Annotating-named-entities-in-Twitter-data-with-crowdsourcing">
  <title><![CDATA[Annotating named entities in Twitter data with crowdsourcing]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/476/Annotating-named-entities-in-Twitter-data-with-crowdsourcing</link>
  <description><![CDATA[We describe our experience using both Amazon Mechanical Turk (MTurk) and Crowd Flower to collect simple named entity annotations for Twitter status updates. Unlike most genres that have traditionally been the focus of named entity experiments, Twitter is far more informal and abbreviated. The collected annotations and annotation techniques will provide a first step towards the full study of named entity recognition in domains like Facebook and Twitter. We also briefly describe how to use MTur...]]></description>
  <dc:date>2010-06-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/475/Unsupervised-techniques-for-discovering-ontology-elements-from-Wikipedia-article-links">
  <title><![CDATA[Unsupervised techniques for discovering ontology elements from Wikipedia article links]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/475/Unsupervised-techniques-for-discovering-ontology-elements-from-Wikipedia-article-links</link>
  <description><![CDATA[We present an unsupervised and unrestricted approach to discovering an infobox like ontology by exploiting the inter-article links within Wikipedia. It discovers new slots and fillers that may not be available in the Wikipedia infoboxes. Our results demonstrate that there are certain types of properties that are evident in the link structure of resources like Wikipedia that can be predicted with high accuracy using little or no linguistic analysis.  The discovered properties can be further us...]]></description>
  <dc:date>2010-06-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/478/Automatic-Discovery-of-Semantic-Relations-using-MindNet">
  <title><![CDATA[Automatic Discovery of Semantic Relations using MindNet]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/478/Automatic-Discovery-of-Semantic-Relations-using-MindNet</link>
  <description><![CDATA[Information extraction deals with extracting entities (such as people,organizations or locations) and named relations between entities (such as "People born-in Country") from text documents. An important challenge in information extraction is the labeling of training data which is usually done manually and is therefore very laborious and in certain cases impractical. This paper introduces a new “model” to extract semantic relations fully automatically from text using the Encarta encyclope...]]></description>
  <dc:date>2010-05-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/461/Ensembles-in-Adversarial-Classification-for-Spam">
  <title><![CDATA[Ensembles in Adversarial Classification for Spam]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/461/Ensembles-in-Adversarial-Classification-for-Spam</link>
  <description><![CDATA[The standard method for combating spam, either in email or on the web, is to train a classifier on manually labeled instances. As the spammers change their tactics, the performance of such classifiers tends to decrease over time. Gathering and labeling more data to periodically retrain the classifier is expensive. We present a method based on an ensemble of classifiers that can detect when its performance might be degrading and retrain itself, all without manual intervention.  Experiments wit...]]></description>
  <dc:date>2009-11-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features">
  <title><![CDATA[Improving Binary Classification on Text Problems using Differential Word Features]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/460/Improving-Binary-Classification-on-Text-Problems-using-Differential-Word-Features</link>
  <description><![CDATA[We describe an efficient technique to weigh word-based features in binary classification tasks and show that it significantly improves classification accuracy on a range of problems. The most common text classification approach uses a document's ngrams (words and short phrases) as its features and assigns feature values equal to their frequency or TFIDF score relative to the training corpus. Our approach uses values computed as the product of an ngram's document frequency and the difference o...]]></description>
  <dc:date>2009-11-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/482/HLTCOE-Approaches-to-Knowledge-Base-Population-at-TAC-2009">
  <title><![CDATA[HLTCOE Approaches to Knowledge Base Population at TAC 2009]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/482/HLTCOE-Approaches-to-Knowledge-Base-Population-at-TAC-2009</link>
  <description><![CDATA[The HLTCOE participated in the entity linking and slot filling tasks at TAC 2009. A machine learning-based approach to entity linking, operating over a wide range of feature types, yielded good performance on the entity linking task. Slot-filling based on sentence selection, application of weak patterns and exploitation of redundancy was ineffective in the slot filling task.]]></description>
  <dc:date>2009-11-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/466/Finding-Semantic-Web-Ontology-Terms-from-Words">
  <title><![CDATA[Finding Semantic Web Ontology Terms from Words]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/466/Finding-Semantic-Web-Ontology-Terms-from-Words</link>
  <description><![CDATA[The Semantic Web was designed to unambiguously define and use ontologies to encode data and knowledge on the Web. Many people find it difficult, however, to write complex RDF statements and queries because it requires familiarity with the appropriate ontologies and the terms they define. We describe a framework that eases the experiences in authoring and querying RDF data, in which we focus on automatically finding a set of appropriate Semantic Web ontology terms from a set of words used as t...]]></description>
  <dc:date>2009-10-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis">
  <title><![CDATA[Delta TFIDF: An Improved Feature Space for Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/448/Delta-TFIDF-An-Improved-Feature-Space-for-Sentiment-Analysis</link>
  <description><![CDATA[Mining opinions and sentiment from social networking sites is a popular application for social media systems. Common approaches use a machine learning system with a bag of words feature set. We present Delta TFIDF, an intuitive general purpose technique to efficiently weight word scores before classification. Delta TFIDF is easy to compute, implement, and understand. We use Support Vector Machines to show that Delta TFIDF significantly improves accuracy for sentiment analysis problems using t...]]></description>
  <dc:date>2009-05-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/432/Cross-Document-Coreference-Resolution-A-Key-Technology-for-Learning-by-Reading">
  <title><![CDATA[Cross-Document Coreference Resolution: A Key Technology for Learning by Reading]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/432/Cross-Document-Coreference-Resolution-A-Key-Technology-for-Learning-by-Reading</link>
  <description><![CDATA[Automatic knowledge base population from text is an important technology for a broad range of approaches to learning by reading. Effective automated knowledge base population depends critically upon coreference resolution of entities across sources. Use of a wide range of features, both those that capture evidence for entity merging and those that argue against merging, can significantly improve machine learning-based cross-document coreference resolution.  Results from the Global Entity Dete...]]></description>
  <dc:date>2009-03-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/431/Using-Wikitology-for-Cross-Document-Entity-Coreference-Resolution">
  <title><![CDATA[Using Wikitology for Cross-Document Entity Coreference Resolution]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/431/Using-Wikitology-for-Cross-Document-Entity-Coreference-Resolution</link>
  <description><![CDATA[We describe the use of the Wikitology knowledge base as a resource for a variety of applications with special focus on a cross-document entity coreference resolution task. This task involves recognizing when entities and relations mentioned in different documents refer to the same object or relation in the world. Wikitology is a knowledge base system constructed with material from Wikipedia, DBpedia and Freebase that includes both unstructured text and semi-structured information.  Wikitology...]]></description>
  <dc:date>2009-03-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/423/Knowledge-Base-Evaluation-for-Semantic-Knowledge-Discovery">
  <title><![CDATA[Knowledge Base Evaluation for Semantic Knowledge Discovery]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/423/Knowledge-Base-Evaluation-for-Semantic-Knowledge-Discovery</link>
  <description><![CDATA[Semantic knowledge discovery has traditionally been evaluated at the text level. For example, evaluations such as MUC and ACE evaluate the information extraction of particular types of semantic roles and relations primarily at the mention level. We suggest that evaluating at the level of a knowledge base (KB) extracted from the text has significant advantages over evaluation at the text level. By knowledge base, we mean the combination of a database, a descriptive schema for the contents of t...]]></description>
  <dc:date>2008-11-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/428/An-Investigation-of-Linguistic-Information-for-Speech-Recognition-Error-Detection">
  <title><![CDATA[An Investigation of Linguistic Information for Speech Recognition Error Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/428/An-Investigation-of-Linguistic-Information-for-Speech-Recognition-Error-Detection</link>
  <description><![CDATA[After several decades of effort, signiﬁcant progress has been made in the area of speech recognition technologies, and various speech-based applications have been developed. However, current speech recognition systems still generate erroneous output, which hinders the wide adoption of speech applications. Given that the goal of error-free output can not be realized in near future, mechanisms for automatically detecting and even correcting speech recognition errors may prove useful for amend...]]></description>
  <dc:date>2008-10-20</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/388/OWL-as-a-Target-for-Information-Extraction-Systems-">
  <title><![CDATA[OWL as a Target for Information Extraction Systems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/388/OWL-as-a-Target-for-Information-Extraction-Systems-</link>
  <description><![CDATA[Current information extraction systems can do a good job of discovering entities, relations and events in natural language text.  The traditional output of such systems is XML, with the ACE Pilot Format (APF) schema as a 
common target.  We are developing a system that will take the output of an information extraction system as APF documents and directly populate a knowledge base with the information extracted.  We report on an initial OWL ontology that covers the APF schema, a simple progra...]]></description>
  <dc:date>2008-04-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/369/Using-a-Natural-Language-Understanding-System-to-Generate-Semantic-Web-Content">
  <title><![CDATA[Using a Natural Language Understanding System to Generate Semantic Web Content]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/369/Using-a-Natural-Language-Understanding-System-to-Generate-Semantic-Web-Content</link>
  <description><![CDATA[We describe our research on automatically generating rich semantic annotations of text and making it available on the Semantic Web. In particular, we discuss the challenges involved in adapting the OntoSem natural language processing system for this purpose. OntoSem, an implementation of the theory of ontological semantics under continuous development for over fifteen years, uses a specially constructed NLP-oriented ontology and an ontologicalsemantic lexicon to translate English text into a ...]]></description>
  <dc:date>2007-11-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/328/Using-a-Natural-Language-Understanding-System-to-Generate-Semantic-Web-Content">
  <title><![CDATA[Using a Natural Language Understanding System to Generate Semantic Web Content]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/328/Using-a-Natural-Language-Understanding-System-to-Generate-Semantic-Web-Content</link>
  <description><![CDATA[We describe our research on automatically generating rich semantic 
annotations of text and making it available on the Semantic Web.
In particular, we discuss the challenges 
involved in adapting the OntoSem natural
language processing system for this purpose. OntoSem, an implementation of
the theory of ontological semantics under continuous development for over 
15 years, uses a specially constructed NLP-oriented ontology and an 
ontological-semantic lexicon to translate English
text...]]></description>
  <dc:date>2006-10-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/301/SemNews-A-Semantic-News-Framework">
  <title><![CDATA[SemNews: A Semantic News Framework]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/301/SemNews-A-Semantic-News-Framework</link>
  <description><![CDATA[SemNews is a semantic news service that monitors different
RSS news feeds and provides structured representations of
themeaning of news. As new content appears, SemNews extracts
the summary from the RSS description and processes
it using OntoSem, which is a sophisticated text understanding
system.]]></description>
  <dc:date>2006-02-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/260/Text-understanding-agents-and-the-Semantic-Web">
  <title><![CDATA[Text understanding agents and the Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/260/Text-understanding-agents-and-the-Semantic-Web</link>
  <description><![CDATA[We discuss the challenges involved in adapting the OntoSem natural
language processing system to the Web.  One set of tasks involves
processing Web documents, translating their computed meaning
representations from the OntoSem's native KR language into the
Semantic Web language OWL, and publishing the results as Web pages and
RSS feeds.  Another set of tasks works in reverse -- querying the Web
for facts needed by OntoSem, translating them from OWL into OntoSem's
native KR language and...]]></description>
  <dc:date>2006-01-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/261/Integrating-Language-Understanding-Agents-Into-the-Semantic-Web">
  <title><![CDATA[Integrating Language Understanding Agents Into the Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/261/Integrating-Language-Understanding-Agents-Into-the-Semantic-Web</link>
  <description><![CDATA[Many intelligent agents need knowledge and information to
support their reasoning and problem solving. The World
Wide Web is a vast, open, accessible and free source of
knowledge, but virtually all of it is encoded as natural language
text � a form difficult for most agents to directly understand.
We describe initial work on adapting a mature language
understanding agent to process Web text and publish
its output in the SemanticWeb language OWL. This approach
adds knowledge on the W...]]></description>
  <dc:date>2005-11-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/114/The-Integrality-of-Speech-in-Multimodal-Interfaces">
  <title><![CDATA[The Integrality of Speech in Multimodal Interfaces]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/114/The-Integrality-of-Speech-in-Multimodal-Interfaces</link>
  <description><![CDATA[A framework of complementary behavior has been proposed which maintains that direct manipulation and speech interfaces have reciprocal strengths and weaknesses. This suggests that user interface performance and acceptance may increase by adopting a multimodal approach that combines speech and direct manipulation. This effort examined the hypothesis that the speed, accuracy, and acceptance of multimodal speech and direct manipulation interfaces will increase when the modalities match the perce...]]></description>
  <dc:date>1998-11-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/337/The-Kernel-Natural-Language-Processing-System">
  <title><![CDATA[The Kernel Natural Language Processing System]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/337/The-Kernel-Natural-Language-Processing-System</link>
  <description><![CDATA[This article describes KERNEL, a text understanding system developed at the Unisys
Center for Advanced Information Technology. KERNEL’s design is motivated by the need
to make complex interactions possible among system modules, and to control the amount
of reasoning done by those modules. We will explain how Kernel’s architecture meets these
needs, and how the architectures of similar systems compare in achieving the same goal.]]></description>
  <dc:date>1993-10-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/560/The-Need-for-User-Models-in-Generating-Expert-System-Explanations">
  <title><![CDATA[The Need for User Models in Generating Expert System Explanations]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/560/The-Need-for-User-Models-in-Generating-Expert-System-Explanations</link>
  <description><![CDATA[An explanation facility is an important component of an expert system, but current systems for the most pa r t have neglected the importance of tailoring a system's explanations to the user.  This paper explores the role of user modelling in generating expert system explanations, making the claim that individualized user models are essential to produce good explanations when the system users vary in their knowledge of the domain, or in their goals, plans, and preferences. To make this argumen...]]></description>
  <dc:date>1988-10-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/287/Modeling-the-user-in-natural-language-systems">
  <title><![CDATA[Modeling the user in natural language systems]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/287/Modeling-the-user-in-natural-language-systems</link>
  <description><![CDATA[For intelligent interactive systems to communicate with humans in a natural manner, they must have knowledge about the system users. This paper explores the role of user modeling in such systems. It begins with a characterization of what a user model is and how it can be used. The types of information that a user model may be required to keep about a user are then identified and discussed. User models themselves can vary greatly depending on the requirements of the situation and the implement...]]></description>
  <dc:date>1988-01-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/347/Natural-language-interactions-with-artificial-experts">
  <title><![CDATA[Natural language interactions with artificial experts]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/347/Natural-language-interactions-with-artificial-experts</link>
  <description><![CDATA[The aim of this paper is to justify why Natural Language (NL) interaction, of a very rich functionality, is critical to the effective use of Expert Systems and to describe what is needed and what has been done to support such interaction. Interactive functions discussed here include defining terms, paraphrasing, correcting misconceptions, avoiding misconceptions, and modifying questions.]]></description>
  <dc:date>1986-07-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/285/The-Semantic-Interpretation-of-Nominal-Compounds">
  <title><![CDATA[The Semantic Interpretation of Nominal Compounds]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/285/The-Semantic-Interpretation-of-Nominal-Compounds</link>
  <description><![CDATA[This paper briefly introduces an approach to the problem of building semantic interpretations of nominal compounds, i.e., sequences of two or more nouns related through modification.  Examples of the kind of nominal compounds dealt with are: "engine repairs", "aircraft flight arrival", "aluminum water pipe" and "noun noun modification".]]></description>
  <dc:date>1980-08-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/486/The-Semantic-Interpretation-of-Compound-Nominals">
  <title><![CDATA[The Semantic Interpretation of Compound Nominals]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/486/The-Semantic-Interpretation-of-Compound-Nominals</link>
  <description><![CDATA[This thesis is an investigation of how a computer can be programmed to understand the class of linguistic phenomena loosely referred to as nominal compounds, i.e. sequences of two or more nouns related through modification.  Examples of the kinds of nominal compounds dealt with are: "engine repairs", "aircraft flight arrival", "aluminum water pump", and "noun noun modification".

The interpretation of nominal compounds is divided into three intertwined subproblems: lexical interpretation (m...]]></description>
  <dc:date>1980-02-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/569/JETS-Achieving-Completeness-through-Coverage-and-Closure">
  <title><![CDATA[JETS: Achieving Completeness through Coverage and Closure]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/569/JETS-Achieving-Completeness-through-Coverage-and-Closure</link>
  <description><![CDATA[Work in progress on JETS, the successor to PLANES, is described. JETS is a natural language question answering system that is intended to interface users to a large relational data base. The architecture is designed to extend the conceptual coverage of JETS to better meet the conversational and data base usage requirements of users. The implementation of JETS is designed to gain a high degree of closure over concept manipulation, contributing to a solution to the problems of perspicuity and s...]]></description>
  <dc:date>1979-08-20</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/306/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild. But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy? For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream. The problem of responding to such changes when they are known to have occurred has been studied extensively. In thi...]]></description>
  <dc:date>2010-09-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/297/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data">
  <title><![CDATA[Improving Accuracy of Named Entity Recognition on Social Media Data]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/297/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data</link>
  <description><![CDATA[We present a system for improving the accuracy of one NLP technique, Named Entity Recognition or NER, on Twitter data by training a recognizer specifically for this type of data.  This training data is obtained from the Amazon Mechanical Turk crowdsourcing platform.]]></description>
  <dc:date>2010-05-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/156/Integrating-Language-Understanding-Agents-into-the-Semantic-Web">
  <title><![CDATA[Integrating Language Understanding Agents into the Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/156/Integrating-Language-Understanding-Agents-into-the-Semantic-Web</link>
  <description><![CDATA[AAAI Fall Symposium session on Agents and Semantic Web presentation at Arlington Virginia Nov 4 2005]]></description>
  <dc:date>2005-11-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/250/Wikitology-Wikipedia-as-an-ontology">
  <title><![CDATA[Wikitology: Wikipedia as an ontology]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/250/Wikitology-Wikipedia-as-an-ontology</link>
  <description><![CDATA[Wikipedia has become an important source of online knowledge for people that is kept up to date and available in many languages. We describe an approach to extracting information from Wikipedia and related sources to construct an ontology and associated knowledge base. The core idea is to use Wikipedia's articles and associated pages as a topic ontology. The benefits of the approach are that the ontology terms are developed through a social process, maintained and kept current by the Wikipedi...]]></description>
  <dc:date>2008-08-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/381/Domain-Independent-Sentiment-Analysis">
  <title><![CDATA[Domain Independent Sentiment Analysis]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/381/Domain-Independent-Sentiment-Analysis</link>
  <description><![CDATA[Domain independent sentiment signals are words or word pairs that are present and have the same sentimental orientation in multiple domains. These words can be easily identified if you have an accurate representation of their in-domain sentimental orientation. If you also have an accurate representation of their sentimental strength then you can use them to correctly classify out of domain documents with reasonable accuracy. In this talk I will present a method to identify domain independent ...]]></description>
  <dc:date>2011-03-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/369/Machine-aided-human-translation-An-automated-system-for-transcribing-dictated-document-translations">
  <title><![CDATA[Machine aided human translation - An automated system for transcribing dictated document translations]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/369/Machine-aided-human-translation-An-automated-system-for-transcribing-dictated-document-translations</link>
  <description><![CDATA[A model is presented for machine aided human translation (MAHT) that integrates source language text and target language acoustic information to produce the text translation of source language document. It is evaluated on a scenario where a human translator dictates a first draft target language translation of a source language document. Information obtained from the source language document, including translation probabilities derived from statistical machine translation (SMT) and named enti...]]></description>
  <dc:date>2010-10-08</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift">
  <title><![CDATA[Detecting Domain Shift]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/357/Detecting-Domain-Shift</link>
  <description><![CDATA[Machine learning systems are typically trained in the lab and then deployed in the wild.  But what happens when the data to which they are exposed in the wild change in a way that hurts accuracy?  For example, a system may be trained to classify movie reviews as either positive or negative (i.e., sentiment classification), but over time book reviews get mixed into the data stream.  The problem of responding to such changes when they are known to have occurred has been studied extensively.  In...]]></description>
  <dc:date>2010-09-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/349/Learning-by-Reading-Automatic-Knowledge-Extraction-Through-Semantic-Analysis-">
  <title><![CDATA[Learning by Reading: Automatic Knowledge Extraction Through Semantic Analysis]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/349/Learning-by-Reading-Automatic-Knowledge-Extraction-Through-Semantic-Analysis-</link>
  <description><![CDATA[Ph.D. Dissertation Defense

To support rich semantic analysis of text, traditional natural language processing tools require access to a cache of static knowledge with both broad coverage and deep meaning.  Acquiring this knowledge by hand is so expensive and error-prone, it has been dubbed the "knowledge acquisition bottleneck".  In this work, we present a method for reducing the impact of this bottleneck by automating the knowledge acquisition task using the novel approach of bootstrappin...]]></description>
  <dc:date>2010-07-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/346/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data">
  <title><![CDATA[Improving Accuracy of Named Entity Recognition on Social Media Data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/346/Improving-Accuracy-of-Named-Entity-Recognition-on-Social-Media-Data</link>
  <description><![CDATA[Master's Thesis Defense

In recent years, social media outlets such as Twitter and Facebook have drawn attention from companies and researchers interested in detecting trends.  The informal nature of status updates from these services leads to a higher volume of updates, because each update takes little care to generate, but each update is usually short and noisy (misspellings, lack of punctuation, non-standard abbreviations and capitalization).  These shortcomings cause traditional Natural...]]></description>
  <dc:date>2010-05-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/345/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text">
  <title><![CDATA[Coarse and Fine Grained Sentiment Analysis of Online Text]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/345/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text</link>
  <description><![CDATA[Sentiment analysis - the automated extraction of expressions of
positive and negative attitudes from text - has received a great
amount of attention over the last ten years. Over the same
period, via the widespread growth in the use of what we have come
to call social media, there has been an explosion in the amount
of publically available user generated text on the Web. This text
has the potential of providing a source of real time, time tagged
sentiments from people all over the glob...]]></description>
  <dc:date>2010-05-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/319/Generic-knowledge-acquisition-and-representation">
  <title><![CDATA[Generic knowledge: acquisition and representation]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/319/Generic-knowledge-acquisition-and-representation</link>
  <description><![CDATA[AI is beginning to make some dents in the "knowledge acquisition bottleneck", the problem of acquiring large amounts of general world knowledge to support language understanding and commonsense reasoning. Two text-based approaches to the problem are (1) to abstract such knowledge from patterns of predication and modification in miscellaneous texts, and (2) to derive such knowledge by direct interpretation of general statements in ordinary language, such as are found in lexicons and resources ...]]></description>
  <dc:date>2009-10-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/282/When-is-a-Translation-not-a-Translation-">
  <title><![CDATA[When is a Translation not a Translation?]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/282/When-is-a-Translation-not-a-Translation-</link>
  <description><![CDATA[A translation is generally taken to be a text that expresses the same
meaning as another text in a different language. But the products of
the best translators reflects a different, if more illusive, goal. I
will seek a somewhat more adequate characterization of translation as
it is actually practiced and discuss its consequences for machine
translation.]]></description>
  <dc:date>2009-02-03</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/256/Information-Extraction-via-Automatic-Generation-of-Semantic-Classifiers">
  <title><![CDATA[Information Extraction via Automatic Generation of Semantic Classifiers]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/256/Information-Extraction-via-Automatic-Generation-of-Semantic-Classifiers</link>
  <description><![CDATA[Information extraction is an important unsolved problem of natural
language processing (NLP). It is the problem of extracting entities
(such as people, organizations or locations) and named relations
between entities (such as "People born-in Country") from text
documents. An important challenge in information extraction is the
labeling of training data which is usually done manually and is
therefore very expensive.

This talk introduces a new "model" to generate training data with
le...]]></description>
  <dc:date>2008-09-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/252/Wikitology-Wikipedia-as-an-ontology-">
  <title><![CDATA[Wikitology: Wikipedia as an ontology]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/252/Wikitology-Wikipedia-as-an-ontology-</link>
  <description><![CDATA[Wikipedia has become an important source of online knowledge for people
that is kept up to date and available in many languages.  We describe
an approach to extracting information from Wikipedia and related
sources to construct an ontology and associated knowledge base. The
core idea is to use Wikipedia's articles and associated pages as a
topic ontology. The benefits of the approach are that the ontology
terms are developed through a social process, maintained and kept
current by the ...]]></description>
  <dc:date>2008-08-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/249/Using-Automatic-Word-Sense-Discrimination-to-generate-a-Semantic-Lexicon">
  <title><![CDATA[Using Automatic Word Sense Discrimination to generate a  Semantic Lexicon]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/249/Using-Automatic-Word-Sense-Discrimination-to-generate-a-Semantic-Lexicon</link>
  <description><![CDATA[Automatic word sense discrimination is the process of distinguishing the 
number of unique senses of a target word in a given corpus. This work 
approaches word sense discrimination as an unsupervised clustering 
problem on the context of the target word in web documents.
Using the features from the computed clusters, the system constructs a 
new lexicon entry for the target word which includes the semantic and 
syntactic constraints for each discriminated sense. The lexicon entries 
a...]]></description>
  <dc:date>2008-07-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/246/Grammatical-Inference-Some-of-the-Questions-Out-There">
  <title><![CDATA[Grammatical Inference: Some of the Questions Out There]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/246/Grammatical-Inference-Some-of-the-Questions-Out-There</link>
  <description><![CDATA[Grammatical Inference is a field concerned with learning
grammars given data about a language.  In this talk we
survey some of the questions being addressed by researchers
in the field.  Some of these are now classical and have been
looked into for some time, others are more recent:

understanding the models and the paradigms:
what does polynomial language learning mean?

learning more complex families of languages

scaling up and using grammatical inference in applications]]></description>
  <dc:date>2008-06-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/210/When-Will-Computers-Understand-Shakespeare-b-font-color-red-CANCELED-9-10-font-b-">
  <title><![CDATA[When Will Computers Understand Shakespeare? CANCELED 9/10]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/210/When-Will-Computers-Understand-Shakespeare-b-font-color-red-CANCELED-9-10-font-b-</link>
  <description><![CDATA[CANCELED 9/10 In this talk I will examine problems encountered in coming to some kind of understanding of one sonnet by Shakespeare (his 64th), ask what it would take to solve these problems computationally, and suggests routes to the solution. The general conclusion is that we are closer to this goal as one might think. Or are we? CANCELED 9/10]]></description>
  <dc:date>2007-09-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/119/Integrating-Language-Understanding-Agents-Into-the-Semantic-Web">
  <title><![CDATA[Integrating Language Understanding Agents Into the Semantic Web]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/119/Integrating-Language-Understanding-Agents-Into-the-Semantic-Web</link>
  <description><![CDATA[Many intelligent agents need knowledge and information to support
their reasoning and problem solving. The World Wide Web is a vast,
open, accessible and free source of knowledge, but virtually all of it
is encoded as natural language text -- a form difficult for most
agents to directly understand.  We describe initial work on adapting a
mature language understanding agent to process Web text and publish
its output in the Semantic Web language OWL.  This approach adds
knowledge on the ...]]></description>
  <dc:date>2005-10-26</dc:date>
 </item>
</rdf:RDF>

