Over-generation errors occur when a keyphrase extraction model correctly determines a candidate keyphrase as a keyphrase because it contains a word that frequently appears in the document but at the same time erroneously outputs other candidates as keyphrases because they contain the same word. To mitigate this issue, we propose a new heterogeneous centrality detection approach (CentralityRank), which extracts keyphrases by simultaneously identifying both implicit and explicit centrality within a heterogeneous graph as the importance score of each candidate. More specifically, CentralityRank detects centrality by taking full advantage of the content within the input document to construct graphs that encompass semantic nodes of varying granularity levels, not limited to just phrases. These additional nodes act as intermediaries between candidate keyphrases, enhancing cross-phrase relations. Furthermore, we introduce a novel adaptive boundary-aware regularization that can leverage the position information of candidate keyphrases, thus influencing the importance of candidate keyphrases. Extensive experimental results demonstrate the superiority of CentralityRank over recent state-of-the-art unsupervised keyphrase extraction baselines across three benchmark datasets.

Mitigating Over-Generation for Unsupervised Keyphrase Extraction with Heterogeneous Centrality Detection

Given the exponential growth in the number of documents on the web in recent years, there is an increasing demand for accurate models to extract keyphrases from such documents. Keyphrase extraction is the task of automatically identifying representative keyphrases from the source document. Typically, candidate keyphrases exhibit latent hierarchical structures embedded with intricate syntactic and semantic information. Moreover, the relationships between candidate keyphrases and the document also form hierarchical structures. Therefore, it is essential to consider these latent hierarchical structures when extracting keyphrases. However, many recent unsupervised keyphrase extraction models overlook this aspect, resulting in incorrect keyphrase extraction. In this paper, we address this issue by proposing a new hyperbolic ranking model (HyperRank). HyperRank is designed to jointly model global and local context information for estimating the importance of each candidate keyphrase within the hyperbolic space, enabling accurate keyphrase extraction. Experimental results demonstrate that HyperRank significantly outperforms recent state-of-the-art baselines.

HyperRank: Hyperbolic Ranking Model for Unsupervised Keyphrase Extraction

Keyphrase Extraction (KE) is a critical component in Natural Language Processing (NLP) systems for selecting a set of phrases from the document that could summarize the important information discussed in the document. Typically, a keyphrase extraction system can significantly accelerate the speed of information retrieval and help people get first-hand information from a long document quickly and accurately. Specifically, keyphrases are capable of providing semantic metadata characterizing documents and producing an overview of the content of a document. In this paper, we introduce keyphrase extraction, present a review of the recent studies based on pre-trained language models, offer interesting insights on the different approaches, highlight open issues, and give a comparative experimental study of popular supervised as well as unsupervised techniques on several datasets. To encourage more instantiations, we release the related files mentioned in this paper.

A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models

A hyperbolic relevance matching model for neural keyphrase extraction.

Hyperbolic Relevance Matching for Neural Keyphrase Extraction

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**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

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Findings

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### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

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EACL 2023

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

5C: Information Extraction 1

technical paper

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Mingyang Song

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SHORT BIO

Presentations

Mitigating Over-Generation for Unsupervised Keyphrase Extraction with Heterogeneous Centrality Detection

HyperRank: Hyperbolic Ranking Model for Unsupervised Keyphrase Extraction

A Survey on Recent Advances in Keyphrase Extraction from Pre-trained Language Models

Hyperbolic Relevance Matching for Neural Keyphrase Extraction

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