Classification Trees for Information Retrieval

Crawford, Stuart L.; Fung, Robert; Appelbaum, Lee A.; Tong, Richard M.

doi:10.1016/b978-1-55860-200-7.50052-0

Cited by 12 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We have used the CART method to compute the decision trees [9]. The Figure 1 shows an example of a decision tree used for the classification process.…”

Section: Cross Validation Campaignmentioning

confidence: 99%

See 1 more Smart Citation

Movies Recommenders Systems: Automation of the Information and Evaluation Phases in a Multi-criteria Decision-Making Process

Plantié¹,

Montmain²,

Dray³

2005

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The authors' interest is focused on advanced recommending functionalities proposed by more and more Internet websites w.r.t. the selection of movies, e-business sites, or any e-purchases. These functionalities often rely on the Internet users' opinions and evaluations. A « movie-recommender » application is presented. Recommender websites generally propose an aggregation of the user's evaluations critics according to different relevant criteria w.r.t. the application. The authors propose an Information Processing System (IPS) to collect, process and manage as automatically as possible these opinions or critics to support this multi criteria evaluation for recommendation. The RS (Recommender System) firstly proposes information extraction techniques in order to classify the available users' critics w.r.t. the criteria implied in the evaluation process and to automatically associate numerical scores to these critics. Then multicriteria techniques are introduced to numerically evaluate, compare and rank the competing movies the critics are reported to. Finally the RS is presented as an interactive Decision-Making Support System (DMSS) relying on a sensibility analysis of the movies ranking. A particular attention is paid to the automation of the information phase in the decision-making process: movie comments cartography according to users' evaluation criteria and attribution of a partial score to each critic considered as the expression of a value judgment in natural language.

show abstract

“…We have used the CART method to compute the decision trees [9]. The Figure 1 shows an example of a decision tree used for the classification process.…”

Section: Cross Validation Campaignmentioning

confidence: 99%

“…To calculate which word should be tested at each node a supervised machine learning procedure is performed: The word associated to a node is computed so as to minimize at this node a measure of the mixing degree of each class. In our case we use the well known "Gini" function described in the CART method [9].…”

Section: Cross Validation Campaignmentioning

confidence: 99%

Movies Recommenders Systems: Automation of the Information and Evaluation Phases in a Multi-criteria Decision-Making Process

Plantié¹,

Montmain²,

Dray³

2005

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…12, and 22, decisions trees in Ref. 7, multivariate linear regression models, 30 nearest neighbor approaches in Ref. 8, and clustering techniques in Refs.…”

Section: Introductionmentioning

confidence: 99%

Integrating WordNet knowledge to supplement training data in semi-supervised agglomerative hierarchical clustering for text categorization

Benkhalifa

Mouradi

Bouyakhf

2001

Int. J. Intell. Syst.

View full text Add to dashboard Cite

The text categorization (TC) is the automated assignment of text documents to predefined categories based on document contents. TC has been an application for many learning approaches, which proved effective. Nevertheless, TC provides many challenges to machine learning. In this paper, we suggest, for text categorization, the integration of external WordNet lexical information to supplement training data for a semi-supervised clustering algorithm which (i) uses a finite design set of labeled data to (ii) help agglomerative hierarchical clustering algorithms (AHC) partition a finite set of unlabeled data and then (iii) terminates without the capacity to classify other objects. This algorithm is the "semi-supervised agglomerative hierarchical clustering algorithm" (ssAHC). Our experiments use Reuters 21578 database and consist of binary classifications for categories selected from the 89 TOPICS classes of the Reuters collection. Using the vector space model (VSM), each document is represented by its original feature vector augmented with external feature vector generated using WordNet. We verify experimentally that the integration of WordNet helps ssAHC improve its performance, effectively addresses the classification of documents into categories with few training documents, and does not interfere with the use of training data.

show abstract

“…More recently Yang [39] obtained a break-even point, where precision equals recall, of .85 on the Reuters newswire collection. Other statistical and machine learning approaches to text categorization include: Maron [24], Crawford et al [10], Yang and Chute [40] Lewis and Gale [21], Apte et al [1], Lewis et al [23], Cohen and Singer [7,8], Hodges et al [16], Moulinier et al [27], Yang [38], Leung and Kan [19], Koller and Sahami [18], and Yang and Pedersen [41]. This paper describes text categorization experiments in the legal domain.…”

Section: Introductionmentioning

confidence: 99%

Automatic categorization of case law

Thompson

2001

Proceedings of the 8th International Conference on Artificial Intelligence and Law

View full text Add to dashboard Cite

This paper describes a series of automatic text categorization experiments with case law documents. Cases are categorized into 40 broad, high-level categories. These results are compared to an existing operational process using Boolean queries manually constructed by domain experts. In this categorization process recall is considered more important than precision. This paper investigates three algorithms that potentially could automate this categorization process: 1) a nearest neighbor-like algorithm, 2) C4.5rules, a machine learning decision tree algorithm; and 3) Ripper, a machine learning rule induction algorithm. The results obtained by Ripper surpass those of the operational process.

show abstract

Classification Trees for Information Retrieval

Cited by 12 publications

References 1 publication

Movies Recommenders Systems: Automation of the Information and Evaluation Phases in a Multi-criteria Decision-Making Process

Movies Recommenders Systems: Automation of the Information and Evaluation Phases in a Multi-criteria Decision-Making Process

Integrating WordNet knowledge to supplement training data in semi-supervised agglomerative hierarchical clustering for text categorization

Automatic categorization of case law

Contact Info

Product

Resources

About