NIF Combinator: Combining NLP Tool Output

Hellmann, Sebastian; Lehmann, Jens; Auer, Sören; Nitzschke, Marcus

doi:10.1007/978-3-642-33876-2_44

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…• Sentiment and emotion analysis from different providers (i.e., a service that uses Vader or one that uses SenticNet [5]) using the same interface (including a NIF-based [6] API and vocabularies [7]). In this way, applications do not depend on the API offered for these services.…”

Section: Functionalitiesmentioning

confidence: 99%

Senpy: A framework for semantic sentiment and emotion analysis services

Sánchez-Rada

Araque

Iglesias

2020

Knowledge-Based Systems

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Section: Functionalitiesmentioning

confidence: 99%

Senpy: A framework for semantic sentiment and emotion analysis services

Sánchez-Rada

Araque

Iglesias

2020

Knowledge-Based Systems

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“…TimeML is the ISO standard 5 for time and event markup and annotation. Other general-purpose annotation standards can also be used to represent TEs, such as the W3C Web Annotations 6 or the NLP Interchange Format 7 (NIF) (Hellmann et al ., 2012). TimeML uses TIMEX3 tags (modeled on previously mentioned TIMEX2) for marking TEs, and distinguishes between different s (namely, , and , the latter being the type associated with sets of recurrent times).…”

Section: Related Workmentioning

confidence: 99%

TempCourt: evaluation of temporal taggers on a new corpus of court decisions

Navas-Loro¹,

Filtz²,

Rodrı́guez-Doncel³

et al. 2019

The Knowledge Engineering Review

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The extraction and processing of temporal expressions (TEs) in textual documents have been extensively studied in several domains; however, for the legal domain it remains an open challenge. This is possibly due to the scarcity of corpora in the domain and the particularities found in legal documents that are highlighted in this paper. Considering the pivotal role played by temporal information when it comes to analyzing legal cases, this paper presents TempCourt, a corpus of 30 legal documents from the European Court of Human Rights, the European Court of Justice, and the United States Supreme Court with manually annotated TEs. The corpus contains two different temporal annotation sets that adhere to the TimeML standard, the first one capturing all TEs and the second dedicated to TEs that are relevant for the case under judgment (thus excluding dates of previous court decisions). The proposed gold standards are subsequently used to compare ten state-of-the-art cross-domain temporal taggers, and to identify not only the limitations of cross-domain temporal taggers but also limitations of the TimeML standard when applied to legal documents. Finally, the paper identifies the need for dedicated resources and the adaptation of existing tools, and specific annotation guidelines that can be adapted to different types of legal documents.

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“…To allow seamless NLP integration, clients should create work flows where the text is normalized (Unicode) at the beginning and tokenization is provided. Figure 2 shows one of the possible workflows that uses an NLP tokenizer in a preprocessing step [8]. Based on the normalization and tokenization, the combined RDF of several tools merges naturally based on the subject URIs as shown in Figure 1.…”

Section: Workflows Modularity and Extensibility Of Nifmentioning

confidence: 99%

Integrating NLP Using Linked Data

Hellmann

Lehmann

Auer

et al. 2013

Lecture Notes in Computer Science

Self Cite

138

106

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We are currently observing a plethora of Natural Language Processing tools and services being made available. Each of the tools and services has its particular strengths and weaknesses, but exploiting the strengths and synergistically combining different tools is currently an extremely cumbersome and time consuming task. Also, once a particular set of tools is integrated, this integration is not reusable by others. We argue that simplifying the interoperability of different NLP tools performing similar but also complementary tasks will facilitate the comparability of results and the creation of sophisticated NLP applications. In this paper, we present the NLP Interchange Format (NIF). NIF is based on a Linked Data enabled URI scheme for identifying elements in (hyper-)texts and an ontology for describing common NLP terms and concepts. In contrast to more centralized solutions such as UIMA and GATE, NIF enables the creation of heterogeneous, distributed and loosely coupled NLP applications, which use the Web as an integration platform. We present several use cases of the second version of the NIF specification (NIF 2.0) and the result of a developer study.

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NIF Combinator: Combining NLP Tool Output

Cited by 7 publications

References 2 publications

Senpy: A framework for semantic sentiment and emotion analysis services

Senpy: A framework for semantic sentiment and emotion analysis services

TempCourt: evaluation of temporal taggers on a new corpus of court decisions

Integrating NLP Using Linked Data

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