Combining keyphrase extraction and lexical diversity to characterize ideas in publication titles

• URL: http://arxiv.org/abs/2208.13978v1
• Date: Tue, 30 Aug 2022 04:08:35 GMT
• Title: Combining keyphrase extraction and lexical diversity to characterize ideas in publication titles
• Abstract summary: We propose to use multiple phrase detection models with the goal to produce a more comprehensive set of keyphrases from the source corpora. We compare the performance of several phrase detection models, analyze the keyphrase sets output of each, and calculate lexical diversity of corpora variants incorporating keyphrases from each model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Beyond bibliometrics, there is interest in characterizing the evolution of the number of ideas in scientific papers. A common approach for investigating this involves analyzing the titles of publications to detect vocabulary changes over time. With the notion that phrases, or more specifically keyphrases, represent concepts, lexical diversity metrics are applied to phrased versions of the titles. Thus changes in lexical diversity are treated as indicators of shifts, and possibly expansion, of research. Therefore, optimizing detection of keyphrases is an important aspect of this process. Rather than just one, we propose to use multiple phrase detection models with the goal to produce a more comprehensive set of keyphrases from the source corpora. Another potential advantage to this approach is that the union and difference of these sets may provide automated techniques for identifying and omitting non-specific phrases. We compare the performance of several phrase detection models, analyze the keyphrase sets output of each, and calculate lexical diversity of corpora variants incorporating keyphrases from each model, using four common lexical diversity metrics.

Related papers

• MUDY: Multi-Granular Dynamic Candidate Contextualization for Unsupervised Keyphrase Extraction [10.20513220388712]
  Keyphrase extraction aims to automatically identify concise phrases that effectively represent a document.<n>Recent methods leveraging pre-trained language models (PLMs) have significantly improved the extraction of keyphrases with strong global semantic relevance.<n>We propose a novel context-centric framework, MUDY, that effectively captures multi-granular contextual salience of candidate keyphrases.
  arXiv  Detail & Related papers  (2026-05-01T12:10:14Z)
• Entropy and type-token ratio in gigaword corpora [0.0]
  We investigate entropy and text-token ratio, two metrics for lexical diversities, in six massive linguistic datasets in English, Spanish, and Turkish. We find a functional relation between entropy and text-token ratio that holds across the corpora under consideration. Our results contribute to the theoretical understanding of text structure and offer practical implications for fields like natural language processing.
  arXiv  Detail & Related papers  (2024-11-15T14:40:59Z)
• SimCKP: Simple Contrastive Learning of Keyphrase Representations [36.88517357720033]
  We propose SimCKP, a simple contrastive learning framework that consists of two stages: 1) An extractor-generator that extracts keyphrases by learning context-aware phrase-level representations in a contrastive manner while also generating keyphrases that do not appear in the document; and 2) A reranker that adapts scores for each generated phrase by likewise aligning their representations with the corresponding document.
  arXiv  Detail & Related papers  (2023-10-12T11:11:54Z)
• ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation [59.91139600152296]
  ParaAMR is a large-scale syntactically diverse paraphrase dataset created by abstract meaning representation back-translation. We show that ParaAMR can be used to improve on three NLP tasks: learning sentence embeddings, syntactically controlled paraphrase generation, and data augmentation for few-shot learning.
  arXiv  Detail & Related papers  (2023-05-26T02:27:33Z)
• Neural Keyphrase Generation: Analysis and Evaluation [47.004575377472285]
  We study various tendencies exhibited by three strong models: T5 (based on a pre-trained transformer), CatSeq-Transformer (a non-pretrained Transformer), and ExHiRD (based on a recurrent neural network) We propose a novel metric framework, SoftKeyScore, to evaluate the similarity between two sets of keyphrases.
  arXiv  Detail & Related papers  (2023-04-27T00:10:21Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Applying Transformer-based Text Summarization for Keyphrase Generation [2.28438857884398]
  Keyphrases are crucial for searching and systematizing scholarly documents. In this paper, we experiment with popular transformer-based models for abstractive text summarization. We show that summarization models are quite effective in generating keyphrases in the terms of the full-match F1-score and BERT.Score. We also investigate several ordering strategies to target keyphrases.
  arXiv  Detail & Related papers  (2022-09-08T13:01:52Z)
• Polling Latent Opinions: A Method for Computational Sociolinguistics Using Transformer Language Models [4.874780144224057]
  We use the capacity for memorization and extrapolation of Transformer Language Models to learn the linguistic behaviors of a subgroup within larger corpora of Yelp reviews. We show that even in cases where a specific keyphrase is limited or not present at all in the training corpora, the GPT is able to accurately generate large volumes of text that have the correct sentiment.
  arXiv  Detail & Related papers  (2022-04-15T14:33:58Z)
• More Than Words: Collocation Tokenization for Latent Dirichlet Allocation Models [71.42030830910227]
  We propose a new metric for measuring the clustering quality in settings where the models differ. We show that topics trained with merged tokens result in topic keys that are clearer, more coherent, and more effective at distinguishing topics than those unmerged models.
  arXiv  Detail & Related papers  (2021-08-24T14:08:19Z)
• Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference [50.768682650658384]
  Keyphrase extraction (KE) aims to summarize a set of phrases that accurately express a concept or a topic covered in a given document. Recent Sequence-to-Sequence (Seq2Seq) based generative framework is widely used in KE task, and it has obtained competitive performance on various benchmarks. In this paper, we propose to adopt the Dynamic Graph Convolutional Networks (DGCN) to solve the above two problems simultaneously.
  arXiv  Detail & Related papers  (2020-10-24T08:11:23Z)
• Exclusive Hierarchical Decoding for Deep Keyphrase Generation [63.357895318562214]
  Keyphrase generation (KG) aims to summarize the main ideas of a document into a set of keyphrases. Previous work in this setting employs a sequential decoding process to generate keyphrases. We propose an exclusive hierarchical decoding framework that includes a hierarchical decoding process and either a soft or a hard exclusion mechanism.
  arXiv  Detail & Related papers  (2020-04-18T02:58:00Z)
• A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts [1.6758573326215689]
  We present a data augmentation strategy and a multi-cascaded model for improved paraphrase detection in short texts. Our model is both wide and deep and provides greater robustness across clean and noisy short texts.
  arXiv  Detail & Related papers  (2019-12-27T12:10:10Z)

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.