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CreatorTitleDescriptionSubjectDate
1 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
2 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
3 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
4 Riloff, Ellen M.Corpus-based identification of non-anaphoric noun phrasesCoreference resolution involves finding antecedents for anaphoric discourse entities, such as definite noun phrases. But many definite noun phrases are not anaphoric because their meaning can be understood from general world knowledge (e.g., "the White House" or "the news media"). We have develope...Corpus-based identification; Non-anaphoric noun phrases; Coreference resolution; MUC-4; Discourse entity; DE1999
5 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
6 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
7 Riloff, Ellen M.Effective information extraction with semantic affinity patterns and relevant regionsWe present an information extraction system that decouples the tasks of finding relevant regions of text and applying extraction patterns. We create a self-trained relevant sentence classifier to identify relevant regions, and use a semantic affinity measure to automatically learn domain-relevant ex...Information extraction; Semantic affinity patterns; Relevant regions; MUC-4 terrorism corpus; ProMed disease outbreak stories2007
8 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
9 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
10 Riloff, Ellen M.Feature subsumption for opinion analysisLexical features are key to many approaches to sentiment analysis and opinion detection. A variety of representations have been used, including single words, multi-word Ngrams, phrases, and lexicosyntactic patterns. In this paper, we use a subsumption hierarchy to formally define different types o...Feature subsumption; Sentiment analysis; Opinion detection; Subsumption hierarchy2006
11 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
12 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
13 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
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 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
16 Riloff, Ellen M.Looking under the hood: tools for diagnosing your question answering engineIn this paper we analyze two question answering tasks : the TREC-8 question answering task and a set of reading comprehension exams. First, we show that Q/A systems perform better when there are multiple answer opportunities per question. Next, we analyze common approaches to two subproblems: ter...TREC-8; Performance2001
17 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
18 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
19 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
20 Riloff, Ellen M.Toward completeness in concept extraction and classificationMany algorithms extract terms from text together with some kind of taxonomic classification (is-a) link. However, the general approaches used today, and specifically the methods of evaluating results, exhibit serious shortcomings. Harvesting without focusing on a specific conceptual area may deliv...Concept extraction; Concept classification2009
21 Riloff, Ellen M.Unified model of phrasal and sentential evidence for information extractionInformation Extraction (IE) systems that extract role fillers for events typically look at the local context surrounding a phrase when deciding whether to extract it. Often, however, role fillers occur in clauses that are not directly linked to an event word. We present a new model for event extract...Information extraction; Phrasal evidence; Sentential evidence; Role fillers; Event extraction; Sentential event recognizer; Plausible roll-filler recognizer2009
22 Riloff, Ellen M.Unsupervised learning of contextual role knowledge for coreference resolutionWe present a coreference resolver called BABAR that uses contextual role knowledge to evaluate possible antecedents for an anaphor. BABAR uses information extraction patterns to identify contextual roles and creates four contextual role knowledge sources using unsupervised learning. These knowledge ...Coreference resolution; Coreference resolver; BABAR; Contextual role knowledge; Unsupervised learning2004
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