This paper presents a method for automatically acquiring the hyponymy lexical relation from unrestricted text. The goal is to avoid the need for pre-encoded knowledge and to apply the method across a wide range of text. The authors identify lexico-syntactic patterns that are easily recognizable, frequent, and occur across text genres. These patterns clearly indicate the hyponymy relation. They describe a method for discovering these patterns and suggest that other lexical relations can also be acquired in this way. A subset of the acquisition algorithm is implemented, and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications to areas such as information retrieval are suggested. The paper discusses the use of lexico-syntactic patterns to discover hyponymy relations in naturally occurring text. For example, the sentence "The bow lute, such as the Bambara ndang, is plucked and has an individual curved neck for each string" indicates that "Bambara ndang" is a kind of "bow lute". The authors propose that such patterns can be used to automatically acquire hyponymy relations. They compare the results of their method with information found in WordNet, a hand-built thesaurus. They find that their method can be used to verify, critique, or augment existing thesauri. The authors describe several lexico-syntactic patterns that indicate the hyponymy relation. These patterns are used to extract hyponymy relations from text. They also discuss the challenges of identifying these patterns and the difficulties of determining which modifiers are significant. They also discuss the results of applying their method to Grolier's American Academic Encyclopedia and the New York Times text. They find that their method can be used to acquire a large number of hyponymy relations. They also discuss the challenges of automatically inserting these relations into the hierarchy and the need for lexical disambiguation algorithms. The authors conclude that their method is a low-cost approach for automatically acquiring semantic lexical relations from unrestricted text. This method is meant to provide an incremental step toward the larger goals of natural language processing. Their approach is complementary to statistically based approaches that find semantic relations between terms. They show that their approach is also useful as a critiquing component for existing knowledge bases and lexicons. They plan to test the pattern discovery algorithm on more relations and on languages other than English. They also plan to analyze the noun phrases that are acquired and to explore the effects of various kinds of modifiers on the appropriateness of the noun phrase. They plan to do this in the context of analyzing environmental impact reports.This paper presents a method for automatically acquiring the hyponymy lexical relation from unrestricted text. The goal is to avoid the need for pre-encoded knowledge and to apply the method across a wide range of text. The authors identify lexico-syntactic patterns that are easily recognizable, frequent, and occur across text genres. These patterns clearly indicate the hyponymy relation. They describe a method for discovering these patterns and suggest that other lexical relations can also be acquired in this way. A subset of the acquisition algorithm is implemented, and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications to areas such as information retrieval are suggested. The paper discusses the use of lexico-syntactic patterns to discover hyponymy relations in naturally occurring text. For example, the sentence "The bow lute, such as the Bambara ndang, is plucked and has an individual curved neck for each string" indicates that "Bambara ndang" is a kind of "bow lute". The authors propose that such patterns can be used to automatically acquire hyponymy relations. They compare the results of their method with information found in WordNet, a hand-built thesaurus. They find that their method can be used to verify, critique, or augment existing thesauri. The authors describe several lexico-syntactic patterns that indicate the hyponymy relation. These patterns are used to extract hyponymy relations from text. They also discuss the challenges of identifying these patterns and the difficulties of determining which modifiers are significant. They also discuss the results of applying their method to Grolier's American Academic Encyclopedia and the New York Times text. They find that their method can be used to acquire a large number of hyponymy relations. They also discuss the challenges of automatically inserting these relations into the hierarchy and the need for lexical disambiguation algorithms. The authors conclude that their method is a low-cost approach for automatically acquiring semantic lexical relations from unrestricted text. This method is meant to provide an incremental step toward the larger goals of natural language processing. Their approach is complementary to statistically based approaches that find semantic relations between terms. They show that their approach is also useful as a critiquing component for existing knowledge bases and lexicons. They plan to test the pattern discovery algorithm on more relations and on languages other than English. They also plan to analyze the noun phrases that are acquired and to explore the effects of various kinds of modifiers on the appropriateness of the noun phrase. They plan to do this in the context of analyzing environmental impact reports.