POS Tagging and Its Applications for Mathematics

Schöneberg, Ulf; Sperber, Wolfram

doi:10.1007/978-3-319-08434-3_16

Cited by 10 publications

(12 citation statements)

References 5 publications

(2 reference statements)

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“…Part-of-Speech Tagging (POS Tagging) assigns a tag to each word in a given text [15]. Although the POS Tagging task is mainly a tool for text processing, it can be adjusted to scientific documents with mathematical expressions [29,26]. Therefore, we tag math-related tokens of the text with math specific tags [29].…”

Section: Find Definiens Candidatesmentioning

confidence: 99%

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Semantification of Identifiers in Mathematics for Better Math Information Retrieval

Schubotz

Grigor’ev

Leich

et al. 2016

Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Mathematical formulae are essential in science, but face challenges of ambiguity, due to the use of a small number of identifiers to represent an immense number of concepts. Corresponding to word sense disambiguation in Natural Language Processing, we disambiguate mathematical identifiers. By regarding formulae and natural text as one monolithic information source, we are able to extract the semantics of identifiers in a process we term Mathematical Language Processing (MLP). As scientific communities tend to establish standard (identifier) notations, we use the document domain to infer the actual meaning of an identifier. Therefore, we adapt the software development concept of namespaces to mathematical notation. Thus, we learn namespace definitions by clustering the MLP results and mapping those clusters to subject classification schemata. In addition, this gives fundamental insights into the usage of mathematical notations in science, technology, engineering and mathematics. Our gold standard based evaluation shows that MLP extracts relevant identifierdefinitions. Moreover, we discover that identifier namespaces improve the performance of automated identifier-definition extraction, and elevate it to a level that cannot be achieved within the document context alone.

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Section: Find Definiens Candidatesmentioning

confidence: 99%

“…Although the POS Tagging task is mainly a tool for text processing, it can be adjusted to scientific documents with mathematical expressions [29,26]. Therefore, we tag math-related tokens of the text with math specific tags [29]. If a math token is only one identifier, an identifier tag is assigned rather that a formula tag.…”

Section: Find Definiens Candidatesmentioning

confidence: 99%

Semantification of Identifiers in Mathematics for Better Math Information Retrieval

Schubotz

Grigor’ev

Leich

et al. 2016

Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…In 2014, Schoeneberg et al discussed part-of-speech (POS) Tagging and its applications for mathematics [11]. Their goal was to adapt NLP methods to the special requirements for STEM document content analysis.…”

Section: B Mathematical (Stem) Document Classificationmentioning

confidence: 99%

“…The documents contain about 60 million mathematical formulae, including monomial expressions, e.g., x or t 2 . The disc size of the dataset is about 174 GB uncompressed and is intended to be used for Information Retrieval research tasks, such as Natural Language Processing, text analysis, and mathematical expression tree structure search 11 .…”

Section: A Ntcir Arxiv Datasetmentioning

confidence: 99%

Towards Explaining STEM Document Classification using Mathematical Entity Linking

Scharpf,

Schubotz,

Gipp

2021

Preprint

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Document subject classification is essential for structuring (digital) libraries and allowing readers to search within a specific field. Currently, the classification is typically made by human domain experts. Semi-supervised Machine Learning algorithms can support them by exploiting the labeled data to predict subject classes for unclassified new documents. However, while humans partly do, machines mostly do not explain the reasons for their decisions. Recently, explainable AI research to address the problem of Machine Learning decisions being a black box has increasingly gained interest. Explainer models have already been applied to the classification of natural language texts, such as legal or medical documents. Documents from Science, Technology, Engineering, and Mathematics (STEM) disciplines are more difficult to analyze, since they contain both textual and mathematical formula content. In this paper, we present first advances towards STEM document classification explainability using classical and mathematical Entity Linking. We examine relationships between textual and mathematical subject classes and entities, mining a collection of documents from the arXiv preprint repository (NTCIR and zbMATH dataset). The results indicate that mathematical entities have the potential to provide high explainability as they are a crucial part of a STEM document.

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“…Perhaps the area of mathematical software with the greatest potential for machine learning applications is Mathematical Knowledge Management (MKM) [12] since many of the tasks are similar to Natural Language Processing (NLP) where machine learning has seen extensive use. For example, [35] describes the automatic identification of a suitable top level from the Mathematics Subject Classification (MSC) system for thousands of articles using an SVM; while [29] describes how NLP techniques were adapted to build a part of speech tagger used for key phrase extraction in the database zbMATH.…”

Section: Mathematical Knowledge Managementmentioning

confidence: 99%

Machine Learning for Mathematical Software

England

2018

Lecture Notes in Computer Science

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While there has been some discussion on how Symbolic Computation could be used for AI there is little literature on applications in the other direction. However, recent results for quantifier elimination suggest that, given enough example problems, there is scope for machine learning tools like Support Vector Machines to improve the performance of Computer Algebra Systems. We survey the authors own work and similar applications for other mathematical software. It may seem that the inherently probabilistic nature of machine learning tools would invalidate the exact results prized by mathematical software. However, algorithms and implementations often come with a range of choices which have no effect on the mathematical correctness of the end result but a great effect on the resources required to find it, and thus here, machine learning can have a significant impact.

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POS Tagging and Its Applications for Mathematics

Cited by 10 publications

References 5 publications

Semantification of Identifiers in Mathematics for Better Math Information Retrieval

Semantification of Identifiers in Mathematics for Better Math Information Retrieval

Towards Explaining STEM Document Classification using Mathematical Entity Linking

Machine Learning for Mathematical Software

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