Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context

• URL: http://arxiv.org/abs/2109.07293v1
• Date: Wed, 15 Sep 2021 13:41:10 GMT
• Title: Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context
• Authors: Xinnian Liang and Shuangzhi Wu and Mu Li and Zhoujun Li
• Abstract summary: Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks. In this paper, we propose a novel method for UKE, where local and global contexts are jointly modeled.
• Score: 25.3472693740778
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks. Generally, these methods simply calculate similarities between phrase embeddings and document embedding, which is insufficient to capture different context for a more effective UKE model. In this paper, we propose a novel method for UKE, where local and global contexts are jointly modeled. From a global view, we calculate the similarity between a certain phrase and the whole document in the vector space as transitional embedding based models do. In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices. Then, we proposed a new centrality computation method to capture local salient information based on the graph structure. Finally, we further combine the modeling of global and local context for ranking. We evaluate our models on three public benchmarks (Inspec, DUC 2001, SemEval 2010) and compare with existing state-of-the-art models. The results show that our model outperforms most models while generalizing better on input documents with different domains and length. Additional ablation study shows that both the local and global information is crucial for unsupervised keyphrase extraction tasks.

Related papers

• Grounding Everything: Emerging Localization Properties in Vision-Language Transformers [51.260510447308306]
  We show that pretrained vision-language (VL) models allow for zero-shot open-vocabulary object localization without any fine-tuning. We propose a Grounding Everything Module (GEM) that generalizes the idea of value-value attention introduced by CLIPSurgery to a self-self attention path. We evaluate the proposed GEM framework on various benchmark tasks and datasets for semantic segmentation.
  arXiv  Detail & Related papers  (2023-12-01T19:06:12Z)
• Coalescing Global and Local Information for Procedural Text Understanding [70.10291759879887]
  A complete procedural understanding solution should combine three core aspects: local and global views of the inputs, and global view of outputs. In this paper, we propose Coalescing Global and Local InformationCG, a new model that builds entity and time representations. Experiments on a popular procedural text understanding dataset show that our model achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2022-08-26T19:16:32Z)
• Grounding Visual Representations with Texts for Domain Generalization [9.554646174100123]
  Cross-modality supervision can be successfully used to ground domain-invariant visual representations. Our proposed method achieves state-of-the-art results and ranks 1st in average performance for five multi-domain datasets.
  arXiv  Detail & Related papers  (2022-07-21T03:43:38Z)
• Capturing Structural Locality in Non-parametric Language Models [85.94669097485992]
  We propose a simple yet effective approach for adding locality information into non-parametric language models. Experiments on two different domains, Java source code and Wikipedia text, demonstrate that locality features improve model efficacy.
  arXiv  Detail & Related papers  (2021-10-06T15:53:38Z)
• Global Aggregation then Local Distribution for Scene Parsing [99.1095068574454]
  We show that our approach can be modularized as an end-to-end trainable block and easily plugged into existing semantic segmentation networks. Our approach allows us to build new state of the art on major semantic segmentation benchmarks including Cityscapes, ADE20K, Pascal Context, Camvid and COCO-stuff.
  arXiv  Detail & Related papers  (2021-07-28T03:46:57Z)
• Document-level Relation Extraction as Semantic Segmentation [38.614931876015625]
  Document-level relation extraction aims to extract relations among multiple entity pairs from a document. This paper approaches the problem by predicting an entity-level relation matrix to capture local and global information. We propose a Document U-shaped Network for document-level relation extraction.
  arXiv  Detail & Related papers  (2021-06-07T13:44:44Z)
• Exploiting Global Contextual Information for Document-level Named Entity Recognition [46.99922251839363]
  We propose a model called Global Context enhanced Document-level NER (GCDoc) At word-level, a document graph is constructed to model a wider range of dependencies between words. At sentence-level, for appropriately modeling wider context beyond single sentence, we employ a cross-sentence module. Our model reaches F1 score of 92.22 (93.40 with BERT) on CoNLL 2003 dataset and 88.32 (90.49 with BERT) on Ontonotes 5.0 dataset.
  arXiv  Detail & Related papers  (2021-06-02T01:52:07Z)
• Coarse-to-Fine Entity Representations for Document-level Relation Extraction [28.39444850200523]
  Document-level Relation Extraction (RE) requires extracting relations expressed within and across sentences. Recent works show that graph-based methods, usually constructing a document-level graph that captures document-aware interactions, can obtain useful entity representations. We propose the textbfCoarse-to-textbfFine textbfEntity textbfRepresentation model (textbfCFER) that adopts a coarse-to-fine strategy.
  arXiv  Detail & Related papers  (2020-12-04T10:18:59Z)
• Towards Making the Most of Context in Neural Machine Translation [112.9845226123306]
  We argue that previous research did not make a clear use of the global context. We propose a new document-level NMT framework that deliberately models the local context of each sentence.
  arXiv  Detail & Related papers  (2020-02-19T03:30:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.