Decision trees for entity identification

Chakaravarthy, Venkatesan T.; Pandit, Vinayaka; Roy, Sambuddha; Awasthi, Pranjal; Mohania, Mukesh

doi:10.1145/1921659.1921661

Cited by 16 publications

(1 citation statement)

References 19 publications

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“…Other researchers conducted NER reviews in the chemical [10], clinical [16], biomedicals [17,18], and food domains [19]. Several NER reviews discuss method approaches, namely classification [20,21], decision tree [22], active learning [16], and unsupervised [23]. Thomas et al [24] and Li et al [25] conducted an NER review that specifically uses a deep learning approach.…”

Section: Introductionmentioning

confidence: 99%

Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application

Warto,

Rustad,

Shidik

et al. 2024

Stat., optim. inf. comput.

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Named entity recognition (NER) is one of the preprocessing stages in natural language processing (NLP), which functions to detect and classify entities in the corpus. NER results are used in various NLP applications, including sentiment analysis, text summarization, chatbot, machine translation, and question answering. Several previous reviews partially discussed NER, for instance, NER reviews in specific domains, NER classification, and NER deep learning. This paper provides a comprehensive and systematic review on NER topic studies published from 2011 to 2020. The main contribution of this review is to present a comprehensive systematic literature review on NER from preprocessing techniques, datasets, application domains, feature extraction techniques, approaches, methods, and evaluation techniques. The result concludes that the deep learning approach and the Bi-directional long short-term memory with a conditional random field (Bi-LSTM-CRF) method are the most interesting methods among NER researchers. At the same time, medical and health are NER researchers' most popular domains. These developments have also led to an increasing number of public datasets in the medical and health fields. At the end of this review, we recommend some opportunities and challenges for NER research going forward.

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Section: Introductionmentioning

confidence: 99%

Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application

Warto,

Rustad,

Shidik

et al. 2024

Stat., optim. inf. comput.

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Adaptive Submodular Ranking

Kambadur

Nagarajan

Navidi

2017

Integer Programming and Combinatorial Optimization

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We study a general adaptive ranking problem where an algorithm needs to perform a sequence of actions on a random user, drawn from a known distribution, so as to "satisfy" the user as early as possible. The satisfaction of each user is captured by an individual submodular function, where the user is said to be satisfied when the function value goes above some threshold. We obtain a logarithmic factor approximation algorithm for this adaptive ranking problem, which is the best possible. The adaptive ranking problem has many applications in active learning and ranking: it significantly generalizes previously-studied problems such as optimal decision trees, equivalence class determination, decision region determination and submodular cover. We also present some preliminary experimental results based on our algorithm.

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Improved Approximation Algorithms for the Average-Case Tree Searching Problem

et al. 2012

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We study the following tree search problem: in a given tree T = (V , E) a vertex has been marked and we want to identify it. In order to locate the marked vertex, we can use edge queries. An edge query e asks in which of the two connected components of T \ e the marked vertex lies. The worst-case scenario where one is interested in minimizing the maximum number of queries is well understood, and linear time algorithms are known for finding an optimal search strategy. Here we study the more involved average-case analysis: A function w : V → R+ is given which measures the likelihood for a vertex to be the one marked, and we seek to determine the strategy (decision tree) that minimizes the weighted average number of queries. In a companion paper we prove that the above tree search problem is NP- complete even for the class of trees of bounded diameter or bounded degree. Here, we match this complexity result with a tight algorithmic analysis of the bounded degree instances. We show that any optimal strategy (i.e., one that minimizes the weighted average number of queries) performs at most O(Delta(T )(log |V | + log(w(T )/wmin))) queries in the worst case, where w(T ) is the sum of the likelihoods of the vertices of T , wmin is the minimum positive likelihood over the vertices of T and Delta(T ) is the maximum degree of T . We combine this result with a non-trivial exponential time algorithm to provide an FPTAS for trees with bounded degree. We also show that for unbounded instances a natural greedy strategy attains a 1.62-approximation, improv- ing upon the best known 14-approximation guarantee, previously provided by two of the authors

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Decision trees for entity identification

Cited by 16 publications

References 19 publications

Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application

Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application

Adaptive Submodular Ranking

Improved Approximation Algorithms for the Average-Case Tree Searching Problem

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