A Corpus-Based Approach for Building Semantic Lexicons

By Ellen Riloff, J. Shepherd. 1997

Semantic 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 WordNet and Cyc, many applications require domain-specific lexicons that represent words and categories for a particular topic. In this paper, we present a corpus-based method that can be used to build semantic lexicons for specific categories. The input to the system is a small set of seed words for a category and a representative text corpus. The output is a ranked list of words that are associated with the category. A user then reviews the top-ranked words and decides which ones should be entered in the semantic lexicon. In experiments with five categories, users typically found about 60 words per category in 10-15 minutes to build a core semantic lexicon.

Subfields: Natural Language Processing

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Data-Efficient Image Recognition with Contrastive Predictive Coding (2019, background): “ative examples are usually sampled randomly. Other approaches to semi-supervised learning train simultaneously on labeled and unlabeled data. Many such works have roots in early work on bootstrapping [57] and co-training [6] from text (see [71] for a survey). An increasingly popular approach in deep”
Learning Syntactic Patterns for Automatic Hypernym Discovery (2004, background): “Thus a wide variety of recent research has focused on finding methods for automatically learning taxonomic relations and constructing semantic hierarchies [1, 2, 3, 4, 6, 8, 9, 10, 16, 18, 19, 20, 21, 22].”
Web-Scale Distributional Similarity and Entity Set Expansion (2009, methodology): “Finally, semi-supervised methods have shown great promise for identifying and labeling entities (Riloff and Shepherd 1997; Riloff and Jones 1999; Banko et al. 2007; Downey et al. 2007; Pa ş ca et al. 2006; Pa ş ca 2007a; Pa ş ca 2007b; Pa ş ca and Durme 2008).”
Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations (2006, methodology): “Other pattern-based algorithms include (Riloff and Shepherd 1997), who used a semi-automatic method for discovering similar words using a few seed examples, KnowItAll (Etzioni et al. 2005) that performs large-scale extraction of facts from the Web, Mann (2002) who used part of speech patterns to…”
Learning subjective nouns using extraction pattern bootstrapping (2003, methodology): “In recent years several techniques have been developed for semantic lexicon creation (e.g., (Hearst, 1992; Riloff and Shepherd, 1997; Roark and Charniak, 1998; Cara-ballo, 1999)).”
Autonomously semantifying wikipedia (2007, methodology): “They use the TAP knowledge base [27] as the standard ontology, and use it to match instances on the Web.”
Semantic Taxonomy Induction from Heterogenous Evidence (2006, background): “Many algorithms with the potential for automatically extending lexical resources have been proposed, including work in lexical acquisition (Riloff and Shepherd, 1997; Roark and Charniak, 1998) and in discovering instances, named entities, and alternate glosses (Etzioni et al., 2005; Pasc¸a, 2005).”
A Rule-based Question Answering System for Reading Comprehension Tests (2000, background): “, (Rilo and Shepherd, 1997; Roark and Charniak, 1998)) to generate bigger and better semantic lexicons.”
From distributional to semantic similarity (2004, methodology): “In these ways, it is also similar to the bootstrapping techniques for learning members of a particular class (Riloff and Shepherd, 1997).”
A Bootstrapping Method for Learning Semantic Lexicons using Extraction Pattern Contexts (2002, methodology): “Three semantic lexicon learners have previously been evaluated on the MUC-4 corpus (Riloff and Shepherd, 1997; Roark and Charniak, 1998; Riloff and Jones, 1999), and of these meta-bootstrapping achieved the best results.”
A survey on sentiment detection of reviews (2009, unknown): “There are mainly two different kinds of lexical filters used (Salvetti, Lewis, & Reichenbach, 2004): one based on hypernymy as provided by WordNet (Budanitsky & Hirst, 2001; Curran, 2002; Devitt & Vogel, 2004; Edmonds, 1999; Edmonds & Hirst, 2002; Fellbaum, 1998; Grefenstette, 1994; Justeson & Slava”
Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping (1999, background): “(Riloff & Shepherd 1997) and 93/257 (36”
Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora (2016, unknown): “Other models rely on general co-occurrence (Turney and Littman, 2003; Riloff and Shepherd, 1997; Igo and Riloff, 2009).”