Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015
Taxonomy plays an important role in many applications by organizing domain knowledge into a hiera... more Taxonomy plays an important role in many applications by organizing domain knowledge into a hierarchy of is-a relations between terms. Previous works on the taxonomic relation identification from text corpora lack in two aspects: 1) They do not consider the trustiness of individual source texts, which is important to filter out incorrect relations from unreliable sources. 2) They also do not consider collective evidence from synonyms and contrastive terms, where synonyms may provide additional supports to taxonomic relations, while contrastive terms may contradict them. In this paper, we present a method of taxonomic relation identification that incorporates the trustiness of source texts measured with such techniques as PageRank and knowledge-based trust, and the collective evidence of synonyms and contrastive terms identified by linguistic pattern matching and machine learning. The experimental results show that the proposed features can consistently improve performance up to 4%-10% of F-measure.
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014
Taxonomies are the backbone of many structured, semantic knowledge resources. Recent works for ex... more Taxonomies are the backbone of many structured, semantic knowledge resources. Recent works for extracting taxonomic relations from text focused on collecting lexical-syntactic patterns to extract the taxonomic relations by matching the patterns to text. These approaches, however, often show low coverage due to the lack of contextual analysis across sentences. To address this issue, we propose a novel approach that collectively utilizes contextual information of terms in syntactic structures such that if the set of contexts of a term includes most of contexts of another term, a subsumption relation between the two terms is inferred. We apply this method to the task of taxonomy construction from scratch, where we introduce another novel graph-based algorithm for taxonomic structure induction. Our experiment results show that the proposed method is well complementary with previous methods of linguistic pattern matching and significantly improves recall and thus F-measure.
Gynostemma pentaphyllum possesses the neuroprotective bioactivity. However, the effect of gypenos... more Gynostemma pentaphyllum possesses the neuroprotective bioactivity. However, the effect of gypenosides on the hypoxia-induced neural damage remains obscure. In this study, Gyp, the active fraction extracted from G. pentaphyllum, and its bioactive compounds as well as the underlying molecular mechanisms were investigated. Eighteen dammarane-type saponins were isolated from Gyp. The absolute configurations of six unreported compounds (13−18) were assessed via ECD analyses. The results of cell viability assay showed that Gyp and its bioactive compounds (13-16 and 18) effectively protected PC12 cells from hypoxia injury. Gyp pre-treatment also improved mice spatial memory impairment caused by hypoxia exposure. At the molecular level, Gyp and its bioactive compounds could activate the signaling pathways of ERK, Akt, and CREB in vitro and in vivo. In summary, Gyp and its bioactive compounds could prevent hypoxia-induced injury via ERK, Akt and CREB signaling pathways.
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015
Taxonomy plays an important role in many applications by organizing domain knowledge into a hiera... more Taxonomy plays an important role in many applications by organizing domain knowledge into a hierarchy of is-a relations between terms. Previous works on the taxonomic relation identification from text corpora lack in two aspects: 1) They do not consider the trustiness of individual source texts, which is important to filter out incorrect relations from unreliable sources. 2) They also do not consider collective evidence from synonyms and contrastive terms, where synonyms may provide additional supports to taxonomic relations, while contrastive terms may contradict them. In this paper, we present a method of taxonomic relation identification that incorporates the trustiness of source texts measured with such techniques as PageRank and knowledge-based trust, and the collective evidence of synonyms and contrastive terms identified by linguistic pattern matching and machine learning. The experimental results show that the proposed features can consistently improve performance up to 4%-10% of F-measure.
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014
Taxonomies are the backbone of many structured, semantic knowledge resources. Recent works for ex... more Taxonomies are the backbone of many structured, semantic knowledge resources. Recent works for extracting taxonomic relations from text focused on collecting lexical-syntactic patterns to extract the taxonomic relations by matching the patterns to text. These approaches, however, often show low coverage due to the lack of contextual analysis across sentences. To address this issue, we propose a novel approach that collectively utilizes contextual information of terms in syntactic structures such that if the set of contexts of a term includes most of contexts of another term, a subsumption relation between the two terms is inferred. We apply this method to the task of taxonomy construction from scratch, where we introduce another novel graph-based algorithm for taxonomic structure induction. Our experiment results show that the proposed method is well complementary with previous methods of linguistic pattern matching and significantly improves recall and thus F-measure.
Gynostemma pentaphyllum possesses the neuroprotective bioactivity. However, the effect of gypenos... more Gynostemma pentaphyllum possesses the neuroprotective bioactivity. However, the effect of gypenosides on the hypoxia-induced neural damage remains obscure. In this study, Gyp, the active fraction extracted from G. pentaphyllum, and its bioactive compounds as well as the underlying molecular mechanisms were investigated. Eighteen dammarane-type saponins were isolated from Gyp. The absolute configurations of six unreported compounds (13−18) were assessed via ECD analyses. The results of cell viability assay showed that Gyp and its bioactive compounds (13-16 and 18) effectively protected PC12 cells from hypoxia injury. Gyp pre-treatment also improved mice spatial memory impairment caused by hypoxia exposure. At the molecular level, Gyp and its bioactive compounds could activate the signaling pathways of ERK, Akt, and CREB in vitro and in vivo. In summary, Gyp and its bioactive compounds could prevent hypoxia-induced injury via ERK, Akt and CREB signaling pathways.
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