A hybrid approach for named entity and sub-type tagging

Srihari, Rohini K.

doi:10.3115/974147.974181

Cited by 96 publications

(61 citation statements)

References 6 publications

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“…The representative machine learning approaches used in NER are Hidden Markov Model (HMM) [1], Maximum Entropy Model [2], Decision Tree [19], Conditional Random Field (CRF) [13] and Support Vector Machine (SVM) [21]. Final category is Hybrid NER that combines both the rule based and the machine based approaches for more accuracy to identify named entities [20].…”

Section: Introductionmentioning

confidence: 99%

Maximum Entropy Approach based Named Entity Recognition in Punjabi Language

Singh¹,

Rani²,

Kaur³

2013

IJCA

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Named Entity Recognition is the task of identifying and classifying named entities into some predefine categories like person, location, organization etc. NER is used in many applications like text summarization, text classification, question answering and machine translation systems etc. For English a lot of work has already been done in the field of NER, where capitalization is a major key for rules, whereas Indian languages do not have such feature. This makes the task difficult for Indian Languages. This work reports about the evaluation of a Named Entity Recognition (NER) system for Punjabi language using the Maximum Entropy Approach (MAXENT). A manually tagged Punjabi news corpus is used for the evaluation which was developed from Punjabi newspaper available online. The training set annotated with a NE tagset of 12 tags is used. A MAXENT based NER system for Punjabi has reported an overall Precision, Recall and FScore values of 90.92%, 72.30% and 80.55% respectively with feature set context word, Part of Speech (POS) information, NE tag of previous word and First name Gazetteer list.

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Section: Introductionmentioning

confidence: 99%

Maximum Entropy Approach based Named Entity Recognition in Punjabi Language

Singh¹,

Rani²,

Kaur³

2013

IJCA

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“…The state-of-the-art exemplified by systems such as NetOwl [Krupka & Hausman 1998], IdentiFinder [Miller et al 1998] and InfoXtract [Srihari et al 2000] has reached near human performance, with 90% or above F-measure. On the other hand, the deep level MUC IE task Scenario Template (ST) is designed to extract detailed information for predefined event scenarios of interest.…”

Section: Introductionmentioning

confidence: 99%

InfoXtract: A Customizable Intermediate Level Information Extraction Engine

Srihari¹,

Li²,

Niu³

et al. 2003

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Information extraction (IE) systems assist analysts to assimilate information from electronic documents. This paper focuses on IE tasks designed to support information discovery applications. Since information discovery implies examining large volumes of documents drawn from various sources for situations that cannot be anticipated a priori, they require IE systems to have breadth as well as depth. This implies the need for a domain-independent IE system that can easily be customized for specific domains: end users must be given tools to customize the system on their own. It also implies the need for defining new intermediate level IE tasks that are richer than the subject-verb-object (SVO) triples produced by shallow systems, yet not as complex as the domain-specific scenarios defined by the Message Understanding Conference (MUC). This paper describes a robust, scalable IE engine designed for such purposes. It describes new IE tasks such as entity profiles, and concept-based general events which represent realistic goals in terms of what can be accomplished in the near-term as well as providing useful, actionable information. These new tasks also facilitate the correlation of output from an IE engine with existing structured data. Benchmarking results for the core engine and applications utilizing the engine are presented.

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“…Litkowski [25] presents manually-derived rules for disambiguating 'of'; Srihari et al [26] present manually-derived rules for disambiguating prepositions used in named entities. Gildea and Jurafsky [27], as well as Blaheta and Charniak [28], address the more general problem of assigning semantic roles to arbitrary constituents of a sentence.…”

Section: Related Workmentioning

confidence: 99%

Classifying Functional Relations in Factotum via WordNet Hypernym Associations

O'Hara

Wiebe

2003

Computational Linguistics and Intelligent Text Processing

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Abstract. This paper describes how to automatically classify the functional relations from the Factotum knowledge base via a statistical machine learning algorithm. This incorporates a method for inferring prepositional relation indicators from corpus data. It also uses lexical collocations (i.e., word associations) and class-based collocations based on the WordNet hypernym relations (i.e., is-subset-of). The result shows substantial improvement over a baseline approach.

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A hybrid approach for named entity and sub-type tagging

Cited by 96 publications

References 6 publications

Maximum Entropy Approach based Named Entity Recognition in Punjabi Language

Maximum Entropy Approach based Named Entity Recognition in Punjabi Language

InfoXtract: A Customizable Intermediate Level Information Extraction Engine

Classifying Functional Relations in Factotum via WordNet Hypernym Associations

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