We propose a tree-similarity-based unsupervised learning method to extract relations between Named Entities from a large raw corpus. Our method regards relation extraction as a clustering problem on shallow parse trees. First, we modify previous tree kernels on relation extraction to estimate the similarity between parse trees more efficiently. Then, the similarity between parse trees is used in a hierarchical clustering algorithm to group entity pairs into different clusters. Finally, each cluster is labeled by an indicative word and unreliable clusters are pruned out. Evaluation on the New York Times (1995) corpus shows that our method outperforms the only previous work by 5 in F-measure. It also shows that our method performs well on both high-frequent and lessfrequent entity pairs. To the best of our knowledge, this is the first work to use a tree similarity metric in relation clustering.
Close-to-nature management (CTNM) is the most promising option for plantation silviculture and has received widespread attention in recent years. Stand density is a key variable in CTNM, as it directly influences growth and yield. Research for the optimal density that maximizes the total harvest has been ongoing. In this paper, a dynamic programming model was applied to the CTNM of Phoebe bournei plantations for the first time to solve the problem of stand density and target tree density control. This paper took Phoebe bournei plantations in Jindong Forest Farm of Hunan Province as the research object. Based on the data of seven consecutive years from 2015 to 2021, Richard’s growth equation was used to fit the height growth equation and basal area growth equation of Phoebe bournei. Stand growth was divided into five development stages according to the forest growth process and characteristics. Stand density and basal area were selected as two-dimensional state variables, and the maximum total harvest in the entire stand growth process was used as the objective function to establish a dynamic programming model. The optimal stand density and target tree density at each growth stage of the stand under three different site conditions were determined. According to the results obtained, the objective forest shape was designed for the stand under three types of site conditions, which can provide a theoretical basis for the CTNM of Phoebe bournei plantations to make the stand achieve the maximum harvest.
Human activities and climate change have resulted in an increasing fragmentation of forest landscapes, and the conflict between biodiversity protection and economic development has become more pronounced. The establishment of forest ecological networks can be a vital part of biodiversity conservation and sustainable forest development. Using Jindong Forest Farm as the study area, this study combines the forest ecological suitability index, morphological spatial pattern analysis, the area method, and the landscape connectivity index (PC, IIC). This will identify ecological source areas in the study area, extract ecological corridors using the minimum cumulative resistance model and the gravity model, and construct a forest ecological network with ecological source areas as points and ecological corridors as edges. This study identified 11 forest patches in highly suitable habitat regions as ecological source regions, and 54 potential corridors were extracted. The study’s results show that a careful analysis of the forest landscape’s ecological suitability and morphological spatial pattern provides a scientific method for the rational selection of ecological source regions and serves as a reference for protecting forest species diversity and sustainable forest development.
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