Patent surrogate extraction and evaluation in the context of patent mapping

Tseng, Yuen-Hsien; Wang, Yeong-Ming; Lin, Yu-I; Lin, Chi-Jen; Juang, Dai-Wei

doi:10.1177/0165551507077406

Cited by 37 publications

(14 citation statements)

References 12 publications

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“…Text summarization techniques have been employed in different application domains, such as summaries of news articles, webpages, and patents [32,33]. e authors in [34] presented a text summarization technique to produce summaries from patents. e approach used several features such as sentence position and cue phrases while determining the sentence importance.…”

Section: Related Workmentioning

confidence: 99%

Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Khan

Gul

Uddin

et al. 2020

Scientific Programming

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Information is exploding on the web at exponential pace, so online movie review is becoming a substantial information resource for online users. However, users post millions of movie reviews on regular basis, and it is not possible for users to summarize the reviews. Movie review classification and summarization is one of the challenging tasks in natural language processing. Therefore, an automatic approach is demanded to summarize the vast amount of movie reviews, and it will allow the users to speedily distinguish the positive and negative aspects of a movie. This study has proposed an approach for movie review classification and summarization. For movie review classification, bag-of-words feature extraction technique is used to extract unigrams, bigrams, and trigrams as a feature set from given review documents, and represent the review documents as a vector space model. Next, the Naïve Bayes algorithm is employed to classify the movie reviews (represented as a feature vector) into positive and negative reviews. For the task of movie review summarization, Word2vec feature extraction technique is used to extract features from classified movie review sentences, and then semantic clustering technique is used to cluster semantically related review sentences. Different text features are used to calculate the salience score of each review sentence in clusters. Finally, the top-ranked sentences are chosen based on highest salience scores to produce the extractive summary of movie reviews. Experimental results reveal that the proposed machine learning approach is superior than other state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Khan

Gul

Uddin

et al. 2020

Scientific Programming

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“…ere are many application domains where ES techniques are used, for instance, summaries of websites, patents records, and news stories [25,26]. Tseng et al [27] proposed a feature based summarization method for patents and used several features like cue phrases and sentence location to identify the relevant sentences. Trappey et al [28] combined ontology tree structure and TF-IDF techniques to extract the keywords for selection of salient sentences from patent documents; then clustering technique is applied to group salient sentences to form a summary.…”

Section: Related Workmentioning

confidence: 99%

Sentence Embedding Based Semantic Clustering Approach for Discussion Thread Summarization

et al. 2020

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Huge data on the web come from discussion forums, which contain millions of threads. Discussion threads are a valuable source of knowledge for Internet users, as they have information about numerous topics. The discussion thread related to single topic comprises a huge number of reply posts, which makes it hard for the forum users to scan all the replies and determine the most relevant replies in the thread. At the same time, it is also hard for the forum users to manually summarize the bulk of reply posts in order to get the gist of discussion thread. Thus, automatically extracting the most relevant replies from discussion thread and combining them to form a summary are a challenging task. With this motivation behind, this study has proposed a sentence embedding based clustering approach for discussion thread summarization. The proposed approach works in the following fashion: At first, word2vec model is employed to represent reply sentences in the discussion thread through sentence embeddings/sentence vectors. Next, K-medoid clustering algorithm is applied to group semantically similar reply sentences in order to reduce the overlapping reply sentences. Finally, different quality text features are utilized to rank the reply sentences in different clusters, and then the high-ranked reply sentences are picked out from all clusters to form the thread summary. Two standard forum datasets are used to assess the effectiveness of the suggested approach. Empirical results confirm that the proposed sentence based clustering approach performed superior in comparison to other summarization methods in the context of mean precision, recall, and F-measure.

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“…The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. [20,21]. The methods and techniques for patent analysis are applied according to the characteristics of the structured and unstructured data.…”

Section: Plos Onementioning

confidence: 99%

“…Thus, the patent map plays an important role in formulating strategies because it provides practical and intuitive information [49]. Because text mining technology can be applied to unstructured data processing to build patent maps, patent maps can effectively convey potentially valuable knowledge and explicit information from patents [21,50]. Therefore, it is imperative to develop a patent map because it provides valuable information in a graphical manner, helping people to explore technological opportunities and avoid technological risks.…”

Section: Patent Mapmentioning

confidence: 99%

Research on technology prospect risk of high-tech projects based on patent analysis

Zhang

Liu

2020

PLoS ONE

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The uncertainty of high technology has determined the high-risk character of high-tech projects. Thus, it is of great importance to effectively avoid the risk of high-tech projects by thoroughly analyzing projects' methodologies and fully understanding the technology prospect risk of projects in the feasibility study phase. This study proposes a systematic research framework to identify and analyze the technology prospect risk of projects based on patent analysis. Thus, text mining technology and principal component analysis are used to improve the traditional patent map method and construct a technology prospect risk map. Moreover, patent value evaluation and correlation analysis methods are combined to identify technology potential areas and calculate the value of technology prospect risk. At the same time, an empirical study is conducted with project cases in the field of optical communications, and the technology prospect risk situations of these projects are ascertained through qualitative and quantitative methods. The study is innovative and practical and offers a better combination of analysis methods for current technology development and specific projects.

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Patent surrogate extraction and evaluation in the context of patent mapping

Cited by 37 publications

References 12 publications

Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Summarizing Online Movie Reviews: A Machine Learning Approach to Big Data Analytics

Sentence Embedding Based Semantic Clustering Approach for Discussion Thread Summarization

Research on technology prospect risk of high-tech projects based on patent analysis

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