4 research outputs found
Using text mining to uncover hidden information in historical sources
Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ ΠΎΠ±Π·ΠΎΡ ΡΠΎΠ³ΠΎ, ΠΊΠ°ΠΊ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠ΅ΠΊΡΡΠ° ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ Π΄Π»Ρ Π²ΡΡΠ²Π»Π΅Π½ΠΈΡ ΡΠΊΡΡΡΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π² ΠΈΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΡΡΠ°Ρ
. ΠΠ½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π°ΠΊΡΠ΅Π½ΡΠΈΡΡΠ΅ΡΡΡ Π½Π° ΠΌΠ΅ΡΠΎΠ΄Π΅ ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΌΠΎΠ΄Π΅Π»ΡΡ
ΡΠΌΠ±Π΅Π΄Π΄ΠΈΠ½Π³ΠΎΠ² ΡΠ»ΠΎΠ². Π‘ΡΠ°ΡΡΡ ΠΈΠ»Π»ΡΡΡΡΠΈΡΡΠ΅Ρ, ΠΊΠ°ΠΊ ΡΡΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈΡΡ Π² ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΡΡ
ΠΈΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡΡ
. ΠΠ΅Π»Π°Π΅ΡΡΡ Π²ΡΠ²ΠΎΠ΄ ΠΎ ΡΠΎΠΌ, ΡΡΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠ΅ΠΊΡΡΠ° ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠΎΠ»Π΅Π·Π½ΡΠΌ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠΌ Π΄Π»Ρ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ ΡΠΊΡΡΡΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π² ΠΈΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΡΡΠ°Ρ
.The article presents an overview of how text mining can be employed to reveal hidden information in historical texts. The attention is focused on the method of thematic modeling and word embedding models. The article illustrates how these techniques have been utilized in historical research. It concludes that text mining is a useful tool for uncovering hidden information in historical
A Corpus Approach to Roman Law Based on Justinianβs Digest
Traditional philological methods in Roman legal scholarship such as close reading and strict juristic reasoning have analysed law in extraordinary detail. Such methods, however, have paid less attention to the empirical characteristics of legal texts and occasionally projected an abstract framework onto the sources. The paper presents a series of computer-assisted methods to open new frontiers of inquiry. Using a Python coding environment, we have built a relational database of the Latin text of the Digest, a historical sourcebook of Roman law compiled under the order of Emperor Justinian in 533 CE. Subsequently, we investigated the structure of Roman law by automatically clustering the sections of the Digest according to their linguistic profile. Finally, we explored the characteristics of Roman legal language according to the principles and methods of computational distributional semantics. Our research has discovered an empirical structure of Roman law which arises from the sources themselves and complements the dominant scholarly assumption that Roman law rests on abstract structures. By building and comparing Latin word embeddings models, we were also able to detect a semantic split in words with general and legal sense. These investigations point to a practical focus in Roman law which is consistent with the view that ancient law schools were more interested in training lawyers for practice rather than in philosophical neatness.</jats:p
Vir is to Moderatus as Mulier is to Intemperans. Lemma Embeddings for Latin
This paper presents a new set of lemma embeddings for the Latin language. Embeddings are trained on a manually annotated corpus of texts belonging to the Classical era: different models, architectures and dimensions are tested and evaluated using a novel benchmark for the synonym selection task. A qualitative evaluation is also performed on the embeddings of rare lemmas. In addition, we release vectors pre-trained on the βOpera Maioraβ by Thomas Aquinas, thus providing a resource to analyze Latin in a diachronic perspective