scholarly journals Extended co-citation search: Graph-based document retrieval on a co-citation network containing citation context information

2019 ◽  
Vol 56 (6) ◽  
pp. 102046 ◽  
Author(s):  
Masaki Eto
Keyword(s):  
Citation Network ◽  
Document Retrieval ◽  
Context Information ◽  
Citation Context ◽  
Search Graph

I Know What You Need: Investigating Document Retrieval Effectiveness with Partial Session Contexts

2022 ◽  
Vol 40 (3) ◽  
pp. 1-30
Author(s):  
Procheta Sen ◽  
Debasis Ganguly ◽  
Gareth J. F. Jones
Keyword(s):  
Relevant Information ◽  
Document Retrieval ◽  
Context Information ◽  
Information Need ◽  
Search System ◽  
Query Log ◽  
Sequence Modeling ◽  
Joint Embedding ◽  
One Step ◽  
A Current

Reducing user effort in finding relevant information is one of the key objectives of search systems. Existing approaches have been shown to effectively exploit the context from the current search session of users for automatically suggesting queries to reduce their search efforts. However, these approaches do not accomplish the end goal of a search system—that of retrieving a set of potentially relevant documents for the evolving information need during a search session. This article takes the problem of query prediction one step further by investigating the problem of contextual recommendation within a search session. More specifically, given the partial context information of a session in the form of a small number of queries, we investigate how a search system can effectively predict the documents that a user would have been presented with had he continued the search session by submitting subsequent queries. To address the problem, we propose a model of contextual recommendation that seeks to capture the underlying semantics of information need transitions of a current user’s search context. This model leverages information from a number of past interactions of other users with similar interactions from an existing search log. To identify similar interactions, as a novel contribution, we propose an embedding approach that jointly learns representations of both individual query terms and also those of queries (in their entirety) from a search log data by leveraging session-level containment relationships. Our experiments conducted on a large query log, namely the AOL, demonstrate that using a joint embedding of queries and their terms within our proposed framework of document retrieval outperforms a number of text-only and sequence modeling based baselines.

scholarly journals Representing Context Information for Document Retrieval

2009 ◽  
pp. 239-250 ◽  
Author(s):  
Maya Carrillo ◽  
Esaú Villatoro-Tello ◽  
A. López-López ◽  
Chris Eliasmith ◽  
Manuel Montes-y-Gómez ◽  
...  
Keyword(s):  
Document Retrieval ◽  
Context Information

scholarly journals Learning Document Embeddings with Crossword Prediction

2019 ◽  
Vol 33 ◽  
pp. 9993-9994
Author(s):  
Junyu Luo ◽  
Min Yang ◽  
Ying Shen ◽  
Qiang Qu ◽  
Haixia Chai
Keyword(s):  
Partial Information ◽  
Document Retrieval ◽  
Document Classification ◽  
Experimental Results ◽  
Context Information ◽  
Decoder Architecture ◽  
Text Reconstruction

In this paper, we propose a Document Embedding Network (DEN) to learn document embeddings in an unsupervised manner. Our model uses the encoder-decoder architecture as its backbone, which tries to reconstruct the input document from an encoded document embedding. Unlike the standard decoder for text reconstruction, we randomly block some words in the input document, and use the incomplete context information and the encoded document embedding to predict the blocked words in the document, inspired by the crossword game. Thus, our decoder can keep the balance between the known and unknown information, and consider both global and partial information when decoding the missing words. We evaluate the learned document embeddings on two tasks: document classification and document retrieval. The experimental results show that our model substantially outperforms the compared methods.1.

scholarly journals Predictions of Citations of a Scholarly Paper

2021 ◽  
Vol 9 (8) ◽  
pp. 1735-1738
Author(s):  
Supradeepa Vella
Keyword(s):  
Citation Network ◽  
Word Embedding ◽  
Web Page ◽  
Research Article ◽  
Citation Context ◽  
Citation Relation ◽  
Index Terms ◽  
Article Citation ◽  
Article Impact

Abstract: Bibliometrics is a statistical analysis of written publications such as books or articles. A bibliographic citationis a reference to a book, article, web page, or other published item. Thus citations are useful for identifying the progress ofthe particular work and measuring the quality of the research article. The cited papers are downloaded using the crawler. Fromthe downloaded article, identify article relation by analyzing the citation context of the article. So first extract the citation context from the article. Citation context are classifies based on cue phrases of Simon tufel. Next, identify the relation of unlabeled article by word embedding. After labeling all articles identifythe perspective behind the citation of the article. In this project, citation relation is identified based on cue phrases of Simon tufel finally article impact is quantified based on the citation network formed from citation analysis. Index Terms: bibliometrics, citation, word embedding, article

Automatic Stereotype Activation Is Context Dependent

2010 ◽  
Vol 41 (3) ◽  
pp. 131-136 ◽  
Author(s):  
Catharina Casper ◽  
Klaus Rothermund ◽  
Dirk Wentura
Keyword(s):  
Lexical Decision ◽  
Context Information ◽  
Decision Task ◽  
Social Categories ◽  
Priming Effects ◽  
Stereotype Activation ◽  
Social Stereotypes ◽  
Priming Paradigm ◽  
Automatic Activation ◽  
Mental Schemas

Processes involving an automatic activation of stereotypes in different contexts were investigated using a priming paradigm with the lexical decision task. The names of social categories were combined with background pictures of specific situations to yield a compound prime comprising category and context information. Significant category priming effects for stereotypic attributes (e.g., Bavarians – beer) emerged for fitting contexts (e.g., in combination with a picture of a marquee) but not for nonfitting contexts (e.g., in combination with a picture of a shop). Findings indicate that social stereotypes are organized as specific mental schemas that are triggered by a combination of category and context information.

Impact of Context Information on Metaphor Elaboration

2018 ◽  
Vol 65 (6) ◽  
pp. 370-384 ◽  
Author(s):  
Veronika Lerche ◽  
Ursula Christmann ◽  
Andreas Voss
Keyword(s):  
Diffusion Model ◽  
Processing Speed ◽  
A Priori ◽  
Model Fit ◽  
Verbal Task ◽  
Context Information ◽  
Speed Of Processing ◽  
Good Account ◽  
Dependent Variables ◽  
Decision Settings

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

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