A comparison of NER tools w.r.t. a domain-specific vocabulary

Heuss, Timm; Humm, Bernhard; Henninger, Christian; Rippl, Thomas

doi:10.1145/2660517.2660520

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…Intangible cultural heritage is one of the specific domains that have not been explored yet for the Balkans in regard to NLP and specifically in the case of the Serbian language. Findings from different research studies have implied that specific contexts play a significant role in extracting the information from texts of specific domains (Heuss et al, 2014). This further implies that the entire infrastructurefrom handcrafted rules, specific ontology to dictionaries, using semantic structures and specific vocabulary for the detection of textual phrases potentially useful for NER and IE in generalmust be supplied for Serbian intangible cultural heritage texts.…”

Section: El 385/6mentioning

confidence: 99%

HerCulB: content-based information extraction and retrieval for cultural heritage of the Balkans

Tanasijević

Pavlović-Lažetić

2020

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Purpose The purpose of this paper is to provide a methodology for automatic annotation of a multimedia collection of intangible cultural heritage mostly in the form of interviews. Assigned annotations provide a way to search the collection. Design/methodology/approach Annotation is based on automatic extraction of metadata and is conducted by named entity and topic extraction from textual descriptions with a rule-based approach supported by vocabulary resources, a compiled domain-specific classification scheme and domain-oriented corpus analysis. Findings The proposed methodology for automatic annotation of a collection of intangible cultural heritage, applied on the cultural heritage of the Balkans, has very good results according to F measure, which is 0.87 for the named entity and 0.90 for topic annotation. The overall methodology enables encapsulating domain-specific and language-specific knowledge into collections of finite state transducers and allows further improvements. Originality/value Although cultural heritage has a significant role in the development of identity of a group or an individual, it is one of those specific domains that have not yet been fully explored in case of many languages. A methodology is proposed that can be used for incorporating natural language processing techniques into digital libraries of cultural heritage.

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Section: El 385/6mentioning

confidence: 99%

HerCulB: content-based information extraction and retrieval for cultural heritage of the Balkans

Tanasijević

Pavlović-Lažetić

2020

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“…The work by Steinmetz, Knuth, and Sack (2013) is also focused on the evaluation of semantic annotation tools; however, their attention is centered more on the statistical analysis of different benchmark and dictionary datasets that can be used in the evaluation process. Heuss, Humm, Henninger, and Rippl (2014) compared the performance of several state-of-the-art semantic annotation tools on domain specific texts (namely texts about museum collections). The study found that, on average, each tool achieved roughly just a third of its F1 score on texts covering general/common topics.…”

Section: Defaultmentioning

confidence: 99%

Evolutionary fine-tuning of automated semantic annotation systems

Cuzzola¹,

Jovanović²,

Bagheri³

et al. 2015

Expert Systems with Applications

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a b s t r a c tConsidering the ever-increasing speed at which new textual content is generated, an efficient and effective use of large text corpora requires automated natural language processing and text analysis tools. A subset of such tools, namely automated semantic annotation tools, are capable of interlinking syntactical forms of text with their underlying semantic concepts. The optimal performance of automated semantic annotation tools often depends on tuning the values of the tools' adjustable parameters to the specificities of the annotation task, and particularly to the characteristics of the text to be annotated. Such characteristics include the text domain, terseness or verbosity level, text length, structure and style. Since the default configuration of annotation tools is not suitable for the large variety of input texts that different combinations of these attributes can produce, users often need to adjust the annotators' tunable parameters in order to get the best results. However, the configuration of semantic annotators is presently a tedious and time consuming task as it is primarily based on a manual trial-and-error process. In this paper, we propose a Parameter Tuning Architecture (PTA) for automating the task of configuring parameter values of semantic annotation tools. We describe the core fitness functions of PTA that operate on the quality of the annotations produced, and offer a solution, based on a genetic algorithm, for searching the space of possible parameter values. Our experiments demonstrate that PTA enables effective configuration of parameter values of many semantic annotation tools.

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