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In this work the authors propose a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The authors show that selectivity-based keyword extraction slightly outperforms an extraction based on the standard centrality measures: in/out-degree, betweenness and closeness. Therefore, they include selectivity and its modification – generalized selectivity as node centrality measures in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network. The experimental results point out that selectivity-based keyword extraction has a great potential for the collection-oriented keyword extraction task.

Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard benchmark dataset and a homemade patent dataset to evaluate the performance of PKEA. Our patent dataset includes 2500 patents from five distinct technological fields related to autonomous cars (GPS systems, lidar systems, object recognition systems, radar systems, and vehicle control systems). We compared our method with Frequency, Term Frequency-Inverse Document Frequency (TF-IDF), TextRank and Rapid Automatic Keyword Extraction (RAKE). The experimental results show that our proposed algorithm provides a promising way to extract keywords from patent texts for patent classification.

With growing amounts of available textual data, development of algorithms capable of automatic analysis, categorization, and summarization of these data has become a necessity. In this research, we present a novel algorithm for keyword identification, that is, an extraction of one or multiword phrases representing key aspects of a given document, called Transformer-Based Neural Tagger for Keyword IDentification (TNT-KID). By adapting the transformer architecture for a specific task at hand and leveraging language model pretraining on a domain-specific corpus, the model is capable of overcoming deficiencies of both supervised and unsupervised state-of-the-art approaches to keyword extraction by offering competitive and robust performance on a variety of different datasets while requiring only a fraction of manually labeled data required by the best-performing systems. This study also offers thorough error analysis with valuable insights into the inner workings of the model and an ablation study measuring the influence of specific components of the keyword identification workflow on the overall performance.

With the explosive growth of network information, in order to obtain the information faster and more accurately, this paper proposes a text keyword extraction method based on Bert. Firstly, the key sentence set is extracted from the background material by Bert model as the information supplement to the text. Then, based on the extended text, TF-IDF, text rank and LDA are combined to extract keywords. The experimental results on real science and technology academic paper data sets show that the performance of the fusion multi type feature combination algorithm is better than that of the traditional single algorithm; and the F value of the algorithm is increased by 1.5% by extracting key sentences from background materials, which further improves the effect of key word extraction.

Keyword provide a brief profile of document contents and serve as an important method for quickly obtaining the document’s themes. Traditional keyword extraction methods are mostly based on statistical relationships between words, with no deeper understanding of the words’ structures. In addition, most studies to date performing keyword extraction are based on ranking-related measure values, without considering the cohesion of the extracted keyword set. In this paper, a keyword extraction method based on a semantic hierarchical graph model is proposed. First, the semantic graph for the document is constructed based on the hierarchical extraction of feature terms. Then, the keyword collection of the document is chosen from the constructed semantic graph. The keyword extraction method in this paper fully accounts for both the context of the keywords and the internal structure by which they are related. By mining the deep hidden structure of feature terms, the proposed method can effectively reveal the hierarchical association between terms within the semantic graph and obtain a keyword collection result with high probability. Moreover, several experiments conducted on released datasets show that our method outperforms the existing methods in terms of precision, recall, and F-measure.

In a meeting, it is often desirable to extract the keywords from each utterance as soon as it is spoken. Therefore, this paper proposes a just-in-time keyword extraction from meeting transcripts. The proposed method considers three major factors that make it different from keyword extraction from normal texts. The first factor is the temporal history of the preceding utterances that grants higher importance to recent utterances than older ones, and the second is topic relevance, which focuses only on the preceding utterances relevant to the current utterance. The final factor is the participants. The utterances spoken by the current speaker should be considered more important than those spoken by other participants. The proposed method considers these factors simultaneously under a graph-based keyword extraction with some graph operations. Experiments on two data sets in English and Korean show that consideration of these factors results in improved performance in keyword extraction from meeting transcripts.

With the rapid development of the Internet, a large number of travelers have posted comments on the experience of tourist attractions on the Internet, but the large number of comments generates a large amount of data, which makes it difficult to understand the experience of tourists. For this reason, this paper improves on the basis of the LSTM keyword analysis algorithm. To obtain the attribute words and viewpoint words of implicit states, a neural network is constructed, and the candidate attribute words are refined. Then, four sentiment rules are refined through the memory space of a deep memory network, according to which the sentiment tendency of keywords is analyzed. Synthesize based on the keyword extraction algorithm and keyword sentiment analysis, and propose an algorithm for analyzing tourists’ experience. Finally, the new algorithm is used to analyze the network comments of the Hainan Forest Scenic Area. The results found that the Hainan Forest Scenic Area, with its beautiful forest scenery, gives tourists a pleasant experience. The word frequency ranking of scenic views is 6.8, which is more forward. The cumulative word frequency of “crowded”, “consumption,” and other related items is only 0.559, which is at a lower level, indicating that the shortcomings of this scenic spot lie in the excessive number of tourists and unstandardized consumption items.

The research of Chinese and Japanese machine translation technology is of great practical significance for promoting and inheriting Chinese excellent culture and advancing the development of economy, education, and culture in China and Japan. In this paper, a cross-lingual keyword extraction machine translation model is investigated. The traditional model is improved on the basis of recurrent neural networks RNN and attention mechanism, and the problem of low information memory capacity of LSTM and GRU algorithms is optimized. By introducing the self-attention mechanism, important information can be filtered from a large amount of information, and a keyword recognition system can be constructed. The system covers three levels: word embedding layer, encoding layer, and CRF layer, and the model effectively utilizes the feature extraction mechanism of the Transformer model and introduces the bidirectional mechanism to obtain the richer semantic representation of word vectors. After the above strategies, the scores on the task of extracting the keywords of the discourse system with Chinese characteristics reach up to 89 in word inference and 83 in cultural adaptation, which effectively improves the performance of machine translation compared with other neural machine translators. The keyword “community of destiny” appears frequently in Japanese media reports, and the content involves the excellent cultural essence of the discourse system with Chinese characteristics, which has a benign influence on the dissemination process in Japanese society.

- With the continuous development of the social economy, multimedia has been valued more and more as a computer technology with strong professional technology and high application level. The quality of multimedia professionals is predicted and analyzed for multimedia professional talent through the Internet according to a Chinese keyword extraction algorithm. It realizes the extraction of keywords through hitemet intelligence information acquisition for solving the problem of hitemet information explosion, aiming to solve the talent quality prediction analysis. The prediction and analysis of multimedia professional talent quality play a crucial role hi talent recruitment and development in the ever-evolving multimedia industry. This paper constructed a Fuzzy Secured Hybrid Search (FSHS) for keyword extraction in the Chinese Language. The proposed FSHS model computes the features in the text for the computation of the talent quality prediction for the extraction of the keywords. Through the utilization of the fuzzy logic model, the features in the text are computed and classification is performed classification and extraction of the features. The simulation results show that the Chinese keyword extraction algorithm has a high recall rate and precision rate, and can effectively predict the quality of professional talents.