Revisiting the dependence language model for information retrieval

Maisonnasse, Loïc; Gaussier, Éric; Chevallet, Jean–Pierre

doi:10.1145/1277741.1277863

Cited by 10 publications

(4 citation statements)

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“…is the head word of in L. Maisonnasse et al [13] extend this dependence language model using a syntactic and semantic analysis model. Lee et al [12] also suggested a language model which is based on dependency parse trees generated by a linguistic parser.…”

Section: Related Workmentioning

confidence: 99%

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A quasi-synchronous dependence model for information retrieval

Park

Croft

Smith

2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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Incorporating syntactic features in a retrieval model has had very limited success in the past, with the exception of binary term dependencies. This paper presents a new term dependency modeling approach based on syntactic dependency parsing for both queries and documents. Our model is inspired by a quasi-synchronous stochastic process for machine translation [21]. We model four different types of relationships between syntactically dependent term pairs to perform inexact matching between documents and queries. We also propose a machine learning technique for predicting optimal parameter settings for a retrieval model incorporating syntactic relationships. The results on TREC collections show that the quasi-synchronous dependence model can improve retrieval performance and outperform a strong state-of-art sequential dependence baseline when we use predicted optimal parameters.

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Section: Related Workmentioning

confidence: 99%

“…Third, most previous work [7,13,21,22] treats only a parent-child relation or a head-modifier dependency in the parse tree of a query. On the other hand, in the quasisynchronous model, we expect to cover various dependency relations of terms in both a query and a document.…”

Section: What Are the Arguments For And Againstmentioning

confidence: 99%

A quasi-synchronous dependence model for information retrieval

Park

Croft

Smith

2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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“…Another work [14] has proposed to extend language models with relationships but focusing on texts. These approaches consider features and relationships, but do not consider several points of views according to several features extracted.…”

Section: Related Workmentioning

confidence: 99%

Visual graph modeling for scene recognition and mobile robot localization

Pham

Mulhem

Maisonnasse

et al. 2010

Multimed Tools Appl

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From the issue entitled "Content-based Multimedia Indexing"International audienceImage retrieval and categorization may need to consider several types of visual features and spatial information between them (e.g., different point of views of an image). This paper presents a novel approach that exploits an extension of the language modeling approach from information retrieval to the problem of graph-based image retrieval and categorization. Such versatile graph model is needed to represent the multiple points of views of images. A language model is defined on such graphs to handle a fast graph matching. We present the experiments achieved with several instances of the proposed model on two collections of images: one composed of 3,849 touristic images and another composed of 3,633 images captured by a mobile robot. Experimental results show that using visual graph model (VGM) improves the accuracies of the results of the standard language model (LM) and outperforms the Support Vector Machine (SVM) method

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“…Thus, [3] proposes (a) the use of a dependency parser to represent documents and queries, and (b) an extension of the language modeling approach to deal with such trees. [8,9] further extend this approach with a model compatible with general graphs, as the ones obtained by a conceptual analysis of documents and queries. Other approaches (as [2,4]) have respectively used probabilistic networks and kernels to capture spatial relationships between regions in an image.…”

Section: Visual Language Modelingmentioning

confidence: 99%

MRIM-LIG at ImageCLEF 2009: Robotvision, Image Annotation and Retrieval Tasks

Pham

Maisonnasse

Mulhem

et al. 2010

Lecture Notes in Computer Science

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This paper describes mainly the experiments that have been conducted by the MRIM group at the LIG in Grenoble for the the ImageCLEF 2009 campaign, focusing on the work done for the Robotvision task. The proposal for this task is to study the behaviour of a generative approach inspired by the language model of information retrieval. To fit with the specificity of the Robotvision task, we added post-processing in a way to tackle with the fact that images do belong only to several classes (rooms) and that image are not independent from each others (i.e., the robot cannot in one second be in three different rooms). The results obtained still need improvement, but the use of such language model in the case of Robotvision is showed. Some results related to the Image Retrieval task and the Image annotation task are also presented.

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Revisiting the dependence language model for information retrieval

Abstract: International audienceno abstrac

Cited by 10 publications

References 3 publications

A quasi-synchronous dependence model for information retrieval

A quasi-synchronous dependence model for information retrieval

Visual graph modeling for scene recognition and mobile robot localization

MRIM-LIG at ImageCLEF 2009: Robotvision, Image Annotation and Retrieval Tasks

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