Explaining Semi-Supervised Text Alignment Through Visualization

Meinecke, Christofer; Wrisley, David Joseph; Jänicke, Stefan

doi:10.1109/tvcg.2021.3105899

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…Visualization for NLP and CL Creating and using embeddings in NLP and CL is crucial for representing and capturing the context and content of words, phrases, sentences, and documents. VA + embedding techniques in this set focus on four themes: exploring the semantics and contextualization of embedding spaces [CTL18,LBT * 18,EAKC * 20,MWZ19,SSKEA21, GHM21, BN21, BCS22,VMZL22,LWZ * 23,MM23], active learning and interpretation for language models [LCSEK19, TWB * 20, SH20, ARCL21, LXW * 21, SKB * 21, SCR * 23], data‐driven information retrieval [CWDH09,BMS17,ZSHL18,KOK * 18,DMdO19, RSBV21, PdSP * 22, JWC * 23], and annotation tools [SJB * 17, BNL * 18,PKL * 18,MWJ22].…”

Section: Categorization Of Va + Embedding Approachesmentioning

confidence: 99%

“…This set contains VA tools on urban science [XTYL18, LKJ*20, MZAD*20, MHL*20, BZQ*21, RMH*22, GZRP*22, SNP*22] and for the analysis of various sources of data, including social media [BEF17, XO21, WSP*21, AAM*21, AYL*22], news/creative writing [PS21, HGE22], literature/digital humanities [NKWW22, MWJ22], and human behaviors/gestures [WLHO19,ZWW*22].…”

Section: Categorization Of Va + Embedding Approachesmentioning

confidence: 99%

“…However, lines/links are often used as an augmentation approach to convey relationships between data entities, features, and other attributes. The word “alignment” is frequently used to describe the motivation for integrating embeddings [MWJ22, XWX * 22, AYL * 22], and computing clusters under different time snapshots or other constraints can inform changes or connections. To facilitate the interpretation of clustering results, parallel coordinate plots [XWX * 22, CGH * 22, AYL * 22], line charts [GZRP * 22], and node‐link diagrams [XO21, CGH * 22] are introduced as ways for users to find alignment for individual features, multidimensional attribute values, or within‐cluster and between‐cluster trends and patterns.…”

Section: Categorization Of Va + Embedding Approachesmentioning

confidence: 99%

See 2 more Smart Citations

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

Huang

Witschard

Kucher

et al. 2023

Computer Graphics Forum

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Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term “embedding” when describing the computational approach. Within this context, embeddings are usually (relatively) low‐dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer‐reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023.

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Section: Categorization Of Va + Embedding Approachesmentioning

confidence: 99%

Section: Categorization Of Va + Embedding Approachesmentioning

confidence: 99%

Section: Categorization Of Va + Embedding Approachesmentioning

confidence: 99%

See 1 more Smart Citation

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

Huang

Witschard

Kucher

et al. 2023

Computer Graphics Forum

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show abstract

“…10,12 This ability to group together words with similar meaning can then be exploited for advanced tasks such as, for instance, text alignment. 38,39 Arguably, the single most influential word embedding technology is Word2Vec, which was introduced in 2013, 40 and the current state-of-the-art is the BERT model. 41 There are different approaches on how to use word embeddings to obtain embeddings for sentences or paragraph-sized text, 42 starting from the intuitive (but limited) approach to take the average of the embeddings of each word in the text.…”

Section: Word and Text Embeddingmentioning

confidence: 99%

Interactive optimization of embedding-based text similarity calculations

Witschard

Jusufi

Martins

et al. 2022

Information Visualization

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Comparing text documents is an essential task for a variety of applications within diverse research fields, and several different methods have been developed for this. However, calculating text similarity is an ambiguous and context-dependent task, so many open challenges still exist. In this paper, we present a novel method for text similarity calculations based on the combination of embedding technology and ensemble methods. By using several embeddings, instead of only one, we show that it is possible to achieve higher quality, which in turn is a key factor for developing high-performing applications for text similarity exploitation. We also provide a prototype visual analytics tool which helps the analyst to find optimal performing ensembles and gain insights to the inner workings of the similarity calculations. Furthermore, we discuss the generalizability of our key ideas to fields beyond the scope of text analysis.

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“…Common methods to visualize text reuse patterns are Grid based [38,39], Sequencealigned [40], or Text-oriented [41] Heat Maps. More popular are side-by-side views supported by stream graphs and aligned barcodes [42][43][44]. Line-level variant graphs [45,46] and tabular views [47] can help visualize similarities and differences.…”

Section: Text Alignmentmentioning

confidence: 99%

Explorative Visual Analysis of Rap Music

2021

Self Cite

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Detecting references and similarities in music lyrics can be a difficult task. Crowdsourced knowledge platforms such as Genius. can help in this process through user-annotated information about the artist and the song but fail to include visualizations to help users find similarities and structures on a higher and more abstract level. We propose a prototype to compute similarities between rap artists based on word embedding of their lyrics crawled from Genius. Furthermore, the artists and their lyrics can be analyzed using an explorative visualization system applying multiple visualization methods to support domain-specific tasks.

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Explaining Semi-Supervised Text Alignment Through Visualization

Cited by 5 publications

References 33 publications

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

Interactive optimization of embedding-based text similarity calculations

Explorative Visual Analysis of Rap Music

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