Centrality-Based Approach for Supervised Term Weighting

Shanavas, Niloofer; Wang, Hui; Lin, Zhiwei; Hawe, Glenn I.

doi:10.1109/icdmw.2016.0181

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…To further investigate the effectiveness of our approach, we have compared our results with current state-of-the-art graph-based and non graphbased methods. In Table 3 we compare against CNN for text classification ,without pre-trained word vectors (Kim, 2014), FastText (Joulin et al, 2017), TextRank (Mihalcea and Tarau, 2004), Word Attraction weights based on word2vec similarities (Wang et al, 2015) and Supervised Term Weighting (TW-CRC) by Shanavas et al (2016). Our work produces comparable to state-of-the-art results.…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, as we have noticed from the experimental evaluation, even using simple and easy-to-compute local criteria (e.g., degree), we achieve good classification performance. Shanavas et al (2016) introduced supervised term weighting (TW-CRC) as a method to integrate class information with graphs. Similarly, we create a graph for each class (label), where we add all words of documents belonging to the respective class as nodes and their co-occurrence as edges.…”

Section: Inverse Collection Weight (Icw)mentioning

confidence: 99%

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Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12)

2018

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The workshops in the TextGraphs series have published and promoted the synergy between the field of Graph Theory (GT) and Natural Language Processing (NLP) for over a decade. The target audience of our workshop comprises of researchers working on problems related to either Graph Theory or graphbased algorithms applied to Natural Language Processing, social media, and the Semantic Web.TextGraphs addresses a broad spectrum of research areas within NLP. This is because, besides traditional NLP applications like parsing, word sense disambiguation, semantic role labeling, and information extraction, graph-based solutions also target web-scale applications like information propagation in social networks, rumor proliferation, e-reputation, language dynamics learning, and future events prediction. Following this tradition, this year's TextGraphs also presents research from diverse topics such as lexical and computational semantics, text clustering and classification, and text compresion and summarization, to name a few.The selection process was competitive -we received 17 submissions (9 long and 8 short submissions) and accepted for presentation 8 of them (5 long and 3 short papers), resulting in the overall acceptance rate of 47%.We are pleased to have two excellent invited speakers for this year's event. We thank Gabor Melli and Maximilian Nickel for their enthusiastic acceptance of our invitation. Finally, we are thankful to the members of the program committee for their valuable and high quality reviews. All submissions have benefited from their expert feedback. Their timely contribution was the basis for accepting an excellent list of papers and making the twelfth edition of TextGraphs a success. AbstractWe introduce a machine learning approach for the identification of "white spaces" in scientific knowledge. Our approach addresses this task as link prediction over a graph that contains over 2M influence statements such as "CTCF activates FOXA1", which were automatically extracted using open-domain machine reading. We model this prediction task using graph-based features extracted from the above influence graph, as well as from a citation graph that captures scientific communities. We evaluated the proposed approach through backtesting. Although the data is heavily unbalanced (50 times more negative examples than positives), our approach predicts which influence links will be discovered in the "near future" with a F1 score of 27 points, and a mean average precision of 68%. IntroductionThe amount of scientific knowledge that is publicly available has increased dramatically in the past few years. For example, PubMed, a search engine of biomedical publications, 1 now indexes over 25 million papers, 17 million of which were published between 1990 and the present. This information overload yields two critical problems. First, this exceeds the human capacity to aggregate and interpret the fragments of knowledge published in these papers, which may result in existing solutions to critical problems being overlooked. Swan...

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Section: Resultsmentioning

confidence: 99%

Section: Inverse Collection Weight (Icw)mentioning

confidence: 99%

Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12)

2018

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“…Selain memetakan relasi antar teks seperti kata, frasa, atau kalimat, model graf dapat menyimpan informasi kemunculan bersama antar kata [15]. Terdapat peningkatan akurasi dengan penggunaan model graf sebagai representasi teks dalam permasalahan klasifikasi dokumen [16]. Representasi teks dengan model graf tidak memerlukan pengetahuan spesifik tentang linguistik atau domain tertentu, tetapi tetap memungkinkan adanya integrasi pengetahuan eksternal seperti Wordnet [17].…”

Section: Issn 2301 -4156unclassified

Ekstraksi Frasa Kunci pada Penggabungan Klaster berdasarkan Maximum-Common-Subgraph

Nurilham¹,

Purwitasari²,

Fatichah³

2018

Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI)

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Document clustering based on topic similarities helps users in searching from a collection of scientific articles. Topic labels are necessesary for describing subjects of the document clusters. Clusters with related subjects or contextual similarities can be merged to produce more descriptive labels. Relations between those words in one context can be modelled as a graph. Instead of single word, this paper proposed cluster labeling of phrases from scientific articles with cluster merging based on graph. The proposed method begins with K-Means++ for clustering the scientific articles. Then, the candidates of word phrases from document clusters are extracted using Frequent Phrase Mining which inspired by Apriori algorithm. Each cluster result has a representation graph from those extracted word phrases. An indicator value from each graph shows any similarities of graph structures which is calculated with Maximum Common Subgraph (MCS). Those clusters are merged if there are any structure similarities between them. Topic labels of clusters are keyword phrases extracted from a representation graph of previous merged clusters using TopicRank algorithm. The merging process which becomes the contribution of this paper is considering topic distribution within clusters for phrase extraction. The proposed method evaluation is performed based on topic coherence of the merged clusters label. The results show that proposed method can improve topic coherence on the merged clusters with MCS graph size percentage as the key factor. Further observation shows that merged cluster labels consistent to MCS graph.

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Structure-Based Supervised Term Weighting and Regularization for Text Classification

Shanavas

Wang

Lin

et al. 2019

Natural Language Processing and Information Systems

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Centrality-Based Approach for Supervised Term Weighting

Cited by 6 publications

References 21 publications

Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12)

Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12)

Ekstraksi Frasa Kunci pada Penggabungan Klaster berdasarkan Maximum-Common-Subgraph

Structure-Based Supervised Term Weighting and Regularization for Text Classification

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