Versions Notes

Environment

Python 3.6nltk 3.4.3StanfordCoreNLP 3.9.1.1torch 1.1.0allennlp 0.8.4

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Usage

import nltkfrom embeddings import sent_emb_sif, word_emb_elmofrom model.method import SIFRank, SIFRank_plusfrom stanfordcorenlp import StanfordCoreNLPimport time#download from https://allennlp.org/elmooptions_file = "../auxiliary_data/elmo_2x4096_512_2048cnn_2xhighway_options.json"weight_file = "../auxiliary_data/elmo_2x4096_512_2048cnn_2xhighway_weights.hdf5"porter = nltk.PorterStemmer()ELMO = word_emb_elmo.WordEmbeddings(options_file, weight_file, cuda_device=0)SIF = sent_emb_sif.SentEmbeddings(ELMO, lamda=1.0)en_model = StanfordCoreNLP(r'E:\Python_Files\stanford-corenlp-full-2018-02-27',quiet=True)#download from https://stanfordnlp.github.io/CoreNLP/elmo_layers_weight = [0.0, 1.0, 0.0]text = "Discrete output feedback sliding mode control of second order systems - a moving switching line approach The sliding mode control systems (SMCS) for which the switching variable is designed independent of the initial conditions are known to be sensitive to parameter variations and extraneous disturbances during the reaching phase. For second order systems this drawback is eliminated by using the moving switching line technique where the switching line is initially designed to pass the initial conditions and is subsequently moved towards a predetermined switching line. In this paper, we make use of the above idea of moving switching line together with the reaching law approach to design a discrete output feedback sliding mode control. The main contributions of this work are such that we do not require to use system states as it makes use of only the output samples for designing the controller. and by using the moving switching line a low sensitivity system is obtained through shortening the reaching phase. Simulation results show that the fast output sampling feedback guarantees sliding motion similar to that obtained using state feedback"keyphrases = SIFRank(text, SIF, en_model, N=15,elmo_layers_weight=elmo_layers_weight)keyphrases_ = SIFRank_plus(text, SIF, en_model, N=15, elmo_layers_weight=elmo_layers_weight)print(keyphrases)print(keyphrases_)

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@article{DBLP:journals/access/SunQZWZ20,  author    = {Yi Sun and               Hangping Qiu and               Yu Zheng and               Zhongwei Wang and               Chaoran Zhang},  title     = {SIFRank: {A} New Baseline for Unsupervised Keyphrase Extraction Based               on Pre-Trained Language Model},  journal   = {{IEEE} Access},  volume    = {8},  pages     = {10896--10906},  year      = {2020},  url       = {https://doi.org/10.1109/ACCESS.2020.2965087},  doi       = {10.1109/ACCESS.2020.2965087},  timestamp = {Fri, 07 Feb 2020 12:04:22 +0100},  biburl    = {https://dblp.org/rec/journals/access/SunQZWZ20.bib},  bibsource = {dblp computer science bibliography, https://dblp.org}}