-
Zhou, D.; Lawless, S.; Wu, X.; Zhao, W.; Liu, J.: ¬A study of user profile representation for personalized cross-language information retrieval (2016)
0.06
0.0556544 = product of:
0.1113088 = sum of:
0.1113088 = sum of:
0.07624056 = weight(_text_:language in 3167) [ClassicSimilarity], result of:
0.07624056 = score(doc=3167,freq=6.0), product of:
0.2030952 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.051766515 = queryNorm
0.3753932 = fieldWeight in 3167, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.0390625 = fieldNorm(doc=3167)
0.03506824 = weight(_text_:22 in 3167) [ClassicSimilarity], result of:
0.03506824 = score(doc=3167,freq=2.0), product of:
0.18127751 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.051766515 = queryNorm
0.19345059 = fieldWeight in 3167, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=3167)
0.5 = coord(1/2)
- Abstract
- Purpose - With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non-native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross-language information retrieval (CLIR) systems through the means of personalized query expansion. Design/methodology/approach - The user profiles consist of weighted terms computed by using frequency-based methods such as tf-idf and BM25, as well as various latent semantic models trained on monolingual documents and cross-lingual comparable documents. This paper also proposes an automatic evaluation method for comparing various user profile generation techniques and query expansion methods. Findings - Experimental results suggest that latent semantic-weighted user profile representation techniques are superior to frequency-based methods, and are particularly suitable for users with a sufficient amount of historical data. The study also confirmed that user profiles represented by latent semantic models trained on a cross-lingual level gained better performance than the models trained on a monolingual level. Originality/value - Previous studies on personalized information retrieval systems have primarily investigated user profiles and personalization strategies on a monolingual level. The effect of utilizing such monolingual profiles for personalized CLIR remains unclear. The current study fills the gap by a comprehensive study of user profile representation for personalized CLIR and a novel personalized CLIR evaluation methodology to ensure repeatable and controlled experiments can be conducted.
- Date
- 20. 1.2015 18:30:22
-
Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023)
0.04
0.03954287 = product of:
0.07908574 = sum of:
0.07908574 = sum of:
0.0440175 = weight(_text_:language in 1012) [ClassicSimilarity], result of:
0.0440175 = score(doc=1012,freq=2.0), product of:
0.2030952 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.051766515 = queryNorm
0.21673335 = fieldWeight in 1012, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.0390625 = fieldNorm(doc=1012)
0.03506824 = weight(_text_:22 in 1012) [ClassicSimilarity], result of:
0.03506824 = score(doc=1012,freq=2.0), product of:
0.18127751 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.051766515 = queryNorm
0.19345059 = fieldWeight in 1012, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=1012)
0.5 = coord(1/2)
- Abstract
- With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.
- Date
- 22. 6.2023 14:55:20
-
Liu, J.; Wu, Y.; Zhou, L.: ¬A hybrid method for abstracting newspaper articles (1999)
0.02
0.017607002 = product of:
0.035214003 = sum of:
0.035214003 = product of:
0.07042801 = sum of:
0.07042801 = weight(_text_:language in 4059) [ClassicSimilarity], result of:
0.07042801 = score(doc=4059,freq=2.0), product of:
0.2030952 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.051766515 = queryNorm
0.34677336 = fieldWeight in 4059, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.0625 = fieldNorm(doc=4059)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- This paper introduces a hybrid method for abstracting Chinese text. It integrates the statistical approach with language understanding. Some linguistics heuristics and segmentation are also incorporated into the abstracting process. The prototype system is of a multipurpose type catering for various users with different reqirements. Initial responses show that the proposed method contributes much to the flexibility and accuracy of the automatic Chinese abstracting system. In practice, the present work provides a path to developing an intelligent Chinese system for automating the information
-
Liu, J.; Zhou, Z.; Gao, M.; Tang, J.; Fan, W.: Aspect sentiment mining of short bullet screen comments from online TV series (2023)
0.01
0.011004375 = product of:
0.02200875 = sum of:
0.02200875 = product of:
0.0440175 = sum of:
0.0440175 = weight(_text_:language in 1018) [ClassicSimilarity], result of:
0.0440175 = score(doc=1018,freq=2.0), product of:
0.2030952 = queryWeight, product of:
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.051766515 = queryNorm
0.21673335 = fieldWeight in 1018, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.9232929 = idf(docFreq=2376, maxDocs=44218)
0.0390625 = fieldNorm(doc=1018)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Bullet screen comments (BSCs) are user-generated short comments that appear as real-time overlays on many video platforms, expressing the audience opinions and emotions about different aspects of the ongoing video. Unlike traditional long comments after a show, BSCs are often incomplete, ambiguous in context, and correlated over time. Current studies in sentiment analysis of BSCs rarely address these challenges, motivating us to develop an aspect-level sentiment analysis framework. Our framework, BSCNET, is a pre-trained language encoder-based deep neural classifier designed to enhance semantic understanding. A novel neighbor context construction method is proposed to uncover latent contextual correlation among BSCs over time, and we also incorporate semi-supervised learning to reduce labeling costs. The framework increases F1 (Macro) and accuracy by up to 10% and 10.2%, respectively. Additionally, we have developed two novel downstream tasks. The first is noisy BSCs identification, which reached F1 (Macro) and accuracy of 90.1% and 98.3%, respectively, through fine-tuning the BSCNET. The second is the prediction of future episode popularity, where the MAPE is reduced by 11%-19.0% when incorporating sentiment features. Overall, this study provides a methodology reference for aspect-level sentiment analysis of BSCs and highlights its potential for viewing experience or forthcoming content optimization.
-
Liu, J.: CIP in China : the development and status quo (1996)
0.01
0.010520472 = product of:
0.021040944 = sum of:
0.021040944 = product of:
0.04208189 = sum of:
0.04208189 = weight(_text_:22 in 5528) [ClassicSimilarity], result of:
0.04208189 = score(doc=5528,freq=2.0), product of:
0.18127751 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.051766515 = queryNorm
0.23214069 = fieldWeight in 5528, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.046875 = fieldNorm(doc=5528)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Source
- Cataloging and classification quarterly. 22(1996) no.1, S.69-76
-
Zhang, Y.; Liu, J.; Song, S.: ¬The design and evaluation of a nudge-based interface to facilitate consumers' evaluation of online health information credibility (2023)
0.01
0.00876706 = product of:
0.01753412 = sum of:
0.01753412 = product of:
0.03506824 = sum of:
0.03506824 = weight(_text_:22 in 993) [ClassicSimilarity], result of:
0.03506824 = score(doc=993,freq=2.0), product of:
0.18127751 = queryWeight, product of:
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.051766515 = queryNorm
0.19345059 = fieldWeight in 993, product of:
1.4142135 = tf(freq=2.0), with freq of:
2.0 = termFreq=2.0
3.5018296 = idf(docFreq=3622, maxDocs=44218)
0.0390625 = fieldNorm(doc=993)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Date
- 22. 6.2023 18:18:34
