© Goldwasser/Harel/Nikolaev
iClassifier is a digital research platform designed to analyze classifier systems in scripts and languages. Through a data-mining process, we undertake relational pattern recognition to identify governing rules and document each language studied.
In this stage, we concentrate on graphemic classifiers known in various complex scripts around the globe. We currently work on Ancient Egyptian, Cuneiform scripts (Sumerian), and ancient Chinese scripts. In complex scripts, graphemic classifiers are unpronounced; they are “seen not heard.”
Classifiers are “spandrels”. They were probably born out of the necessity to create segmentation and improve reference tracking in graphic ambiguity cases. Nevertheless, they have developed into a full-fledged system of their own.
Our hypothesis is that every classifier represents a “category head” in the culture’s mind. The iClassifier research platform reveals similarities and differences between cultures. Our studies contribute significantly to a greater understanding of universal vs. culture-specific classification patterns.
We aim to achieve our goal by conducting network analysis methods. We create knowledge organization networks in which classifiers and their host lemmas are the nodes. We then apply various analysis methods, such as identifying clusters with community detection algorithms.
Users can create their dataset using the platform. Furthermore, using iClassifier, one can tokenize an existing dataset, enrich it with classifier marking and annotation, and share the data back for re-use.
Our research addresses the following topics:
1. Identifying the category that each classifier heads and defining its structure (category axis) —
The central members and the fuzzy-edge members in each category. To compare various classifier systems, we assign labels to a category according to the emic information emerging from the scope of the words classified, e.g., [hide & tail] 𓄛.
2. Getting closer to emic lexical meanings of single lexemes (word axis) by defining the range of categories to which a lexical item is assigned.
3. Building classifier-based networks and applying network analysis methods. Tracing compatibility and incompatibility patterns (scale, diameter)and classifier combination patterns (identifying clusters/communities).
4. Creating networks according to specific metadata queries — the classifier system according to script variation (hieratic vs. hieroglyphic), time frame, geography, and other variables annotated for each token in the iClassifier digital research platform.
5. Assessing classifier centrality in culture — classifiers that head large categories versus those that head small ones.
6. Comparative studies on parallel texts, e.g., different manuscripts of the Coffin Texts.
7. The diachrony of knowledge organization: A text in history – similar manuscripts along time, e.g., Pyramid Texts, Coffin texts, Book of the Dead.
8. Researching the longue durée (diachrony) — how do classifier categories emerge, and how do they decline? The dynamics of “successful” categories.
Drawing encompassing mind maps, and creating classifier-based networks:
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Drawing the mind map of the Sumerian script, In collaboration with Prof. Gebhard Selz, University of Vienna. Based on the ePSD2 : electronic Pennsylvania Sumerian Dictionary.
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Drawing the mind map of pEbers in collaboration with Prof. Tanja Pommerening,
Universität Marburg, based on TLA data, courtesy of the "Strukturen und Transformationen des Wortschatzes der ägyptischen Sprache" project, Sächsische Akademie der Wissenschaften zu Leipzig.
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Drawing the mind map represented in the classifiers of Egyptian literary texts in the Middle Kingdom.
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Drawing the mind map of a defined corpus of the Warring States according to classifiers.
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Studying verb classification and exploring how verb valency and argument structure are manifested
by classifiers.
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Comparative studies on Egyptian classifiers and classifiers in ancient Chinese.
Documenting comparable categories, e.g. 𓈗 /水/氵[ᴡᴀᴛᴇʀ] ,
𓀔/子 [ᴄʜɪʟᴅ] or 𓂡/𓀜 /殳/攵/攴 [ʜᴀɴᴅ+sᴛɪᴄᴋ].