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1 Stoddart, JoanRethinking information delivery: using a natural language processing application for point-of-care data discoveryObjective: This paper examines the use of Semantic MEDLINE, a natural language processing application enhanced with a statistical algorithm known as Combo, as a potential decision support tool for clinicians. Semantic MEDLINE summarizes text in PubMed citations, transforming it into compact declarat...2012-01-01
2 Riloff, Ellen M.Information extraction as a stepping stone toward story understandingHistorically story understanding systems have depended on a great deal of handcrafted knowledge. Natural language understanding systems that use conceptual knowledge structures (Schank and Abelson 1977; Cullingford 1978; Wilensky 1978; Carbonell 1979; Lehnert 1981; Kolodner 1983) typically rely on ...Information extraction; Story understanding1999
3 Riloff, Ellen M.OpinionFinder: a system for subjectivity analysisOpinionFinder is a system that performs subjectivity analysis, automatically identifying when opinions, sentiments, speculations and other private states are present in text. Specifically, OpinionFinder aims to identify subjective sentences and to mark various aspects of the subjectivity in the...OpinionFinder; Subjectivity analysis2005
4 Riloff, Ellen M.Corpus-based approach for building semantic lexiconsSemantic knowledge can be a great asset to natural language processing systems, but it is usually hand-coded for each application. Although some semantic information is available in general-purpose knowledge bases such as Word Net and Cyc, many applications require domain-specific lexicons that repr...Corpus-based method; Semantic lexicons1997
5 Riloff, Ellen M.Bootstrapping method for learning semantic lexicons using extraction pattern contextsThis paper describes a bootstrapping algorithm called Basilisk that learns high-quality semantic lexicons for multiple categories. Basilisk begins with an unannotated corpus and seed words for each semantic category, which are then bootstrapped to learn new words for each category. Basilisk hypothe...Basilisk; Bootstrapping method; Semantic lexicons2002
6 Riloff, Ellen M.Automatically constructing a dictionary for information extraction tasksKnowledge-based natural language processing systems have achieved good success with certain tasks but they are often criticized because they depend on a domain-specific dictionary that requires a great deal of manual knowledge engineering. This knowledge engineering bottleneck makes knowledge-based ...Information extraction; Dictionary construction; Knowledge-based systems; AutoSlog; Domain-specific dictionary1993
7 Riloff, Ellen M.Exploiting role-identifying nouns and expressions for information extractionWe present a new approach for extraction pattern learning that exploits role-identifying nouns, which are nouns whose semantics reveal the role that they play in an event (e.g., an "assassin" is a perpetrator). Given a few seed nouns, a bootstrapping algorithm automatically learns role-identifying ...Information extraction; Role-identifying; Nouns; Expressions; Pattern learning; Basilisk bootstrapping algorithm2007
8 Riloff, Ellen M.Automatically generating extraction patterns from untagged textMany corpus-based natural language processing systems rely on text corpora that have been manually annotated with syntactic or semantic tags. In particular, all previous dictionary construction systems for information extraction have used an annotated training corpus or some form of annotated input...Information extraction; Automatically generating; Extraction patterns; Untagged text; Corpus-based; AutoSlog-TS; AutoSlog system; MUC-4; Dictionary construction1996
9 Riloff, Ellen M.Empirical approach to conceptual case frame acquisitionConceptual natural language processing systems usually rely on case frame instantiation to recognize events and role objects in text. But generating a good set of case frames for a domain is time-consuming, tedious, and prone to errors of omission. We have developed a corpus-based algorithm for a...Conceptual case frame acquisition; Case frame instantiation; Corpus-based algorithm1998
10 Riloff, Ellen M.Identifying sources of opinions with conditional random fields and extraction patternsRecent systems have been developed for sentiment classification, opinion recognition, and opinion analysis (e.g., detecting polarity and strength). We pursue another aspect of opinion analysis: identifying the sources of opinions, emotions, and sentiments. We view this problem as an information ext...Sentiment classification; Opinion recognition; Opinion analysis; Conditional random fields; AutoSlog; Sources of opinions2005
11 Riloff, Ellen M.Domain-specific coreference resolution with lexicalized featuresMost coreference resolvers rely heavily on string matching, syntactic properties, and semantic attributes of words, but they lack the ability to make decisions based on individual words. In this paper, we explore the benefits of lexicalized features in the setting of domain-specific coreference reso...2014-01-01
12 Riloff, Ellen M.Empirical study of automated dictionary construction for information extraction in three domainsA primary goal of natural language processing researchers is to develop a knowledge-based natural language processing (NLP) system that is portable across domains. However, most knowledge-based NLP systems rely on a domain-specific dictionary of concepts, which represents a substantial knowledge-en...Information extraction; AutoSlog; Across domains1996
13 Riloff, Ellen M.Conundrums in noun phrase coreference resolution: making sense of the state-of-the-artWe aim to shed light on the state-of-the-art in NP coreference resolution by teasing apart the differences in the MUC and ACE task definitions, the assumptions made in evaluation methodologies, and inherent differences in text corpora. First, we examine three subproblems that play a role in coref...Noun phrase; Coreference resolution; MUC; ACE2009
14 Riloff, Ellen M.Learning extraction patterns for subjective expressionsThis paper presents a bootstrapping process that learns linguistically rich extraction patterns for subjective (opinionated) expressions. High-precision classifiers label unannotated data to automatically create a large training set, which is then given to an extraction pattern learning algorithm. T...Bootstrapping process; Extraction patterns; Subjective expressions; Opinions2003
15 Gardner, Reed M.Medical Informatics at the University of Utah: Applying Research to Real-Life IssuesBiomedical Informatics1999
16 Riloff, Ellen M.Exploiting strong syntactic heuristics and co-training to learn semantic lexiconsWe present a bootstrapping method that uses strong syntactic heuristics to learn semantic lexicons. The three sources of information are appositives, compound nouns, and ISA clauses. We apply heuristics to these syntactic structures, embed them in a bootstrapping architecture, and combine them with...Syntactic heuristics; Semantic lexicons; Bootstrapping method; Appositives; Compound nouns; ISA clauses; Co-training2002
17 Riloff, Ellen M.Learning subjective nouns using extraction pattern bootstrappingWe explore the idea of creating a subjectivity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from objective sentences. First, we use two bootstrapping algorithms that explo...Subjective nouns; Bootstrapping; Extraction patterns; Subjectivity classifier; Naive Bayes classifier2003
18 Riloff, Ellen M.Corpus-based semantic lexicon induction with web-based corroborationVarious techniques have been developed to automatically induce semantic dictionaries from text corpora and from the Web. Our research combines corpus-based semantic lexicon induction with statistics acquired from the Web to improve the accuracy of automatically acquired domain-specific dictionari...Corpus-based; Text corpora; Domain-specific dictionaries; Bootstrapping algorithm2009
19 Riloff, Ellen M.Learning domain-specific information extraction patterns from the webMany information extraction (IE) systems rely on manually annotated training data to learn patterns or rules for extracting information about events. Manually annotating data is expensive, however, and a new data set must be annotated for each domain. So most IE training sets are relatively small. C...Information extraction; Domain-specific; Annotated training sets; MUC-42006
20 Riloff, Ellen M.Corpus-based bootstrapping algorithm for semi-automated semantic lexicon constructionMany applications need a lexicon that represents semantic information but acquiring lexical information is time consuming. We present a corpus-based bootstrapping algorithm that assists users in creating domain-specifi c semantic lexicons quickly. Our algorithm uses a representative text corpus for ...Bootstrapping algorithm; Lexicon construction1999-06
21 Riloff, Ellen M.Learning dictionaries for information extraction by multi-level bootstrappingInformation extraction systems usually require two dictionaries: a semantic lexicon and a dictionary of extraction patterns for the domain. We present a multilevel bootstrapping algorithm that generates both the semantic lexicon and extraction patterns simultaneously. As input, our technique requir...Information extraction; Extraction patterns; Multi-level bootstrapping; Learning dictionaries1999
22 Riloff, Ellen M.Inducing information extraction systems for new languages via cross-language projectionInformation extraction (IE) systems are costly to build because they require development texts, parsing tools, and specialized dictionaries for each application domain and each natural language that needs to be processed. We present a novel method for rapidly creating IE systems for new languages by...Information extraction; IE systems; Cross-language projection; English; French2002
23 Riloff, Ellen M.Semantic class learning from the web with hyponym pattern linkage graphsWe present a novel approach to weakly supervised semantic class learning from the web, using a single powerful hyponym pattern combined with graph structures, which capture two properties associated with pattern-based extractions: popularity and productivity. Intuitively, a candidate is popular if ...Weakly supervised; Semantic class learning; Hyponym pattern; Pattern-based extractions; Class name; Seed instance2008
24 Riloff, Ellen M.Learning to identify reduced passive verb phrases with a shallow parserOur research is motivated by the observation that NLP systems frequently mislabel passive voice verb phrases as being in the active voice when there is no auxiliary verb (e.g., "The man arrested had a long record"). These errors directly impact thematic role recognition and NLP applications that dep...Passive voice; Reduced passive verb phrases; Shallow parser; Learned classifier2008
25 Riloff, Ellen M.Rule-based question answering system for reading comprehension testsWe have developed a rule-based system, Quarc, that can read a short story and find the sentence in the story that best answers a given question. Quarc uses heuristic rules that look for lexical and semantic clues in the question and the story. We have tested Quarc on reading comprehension tests typi...Quarc; Reading comprehension2000
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