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PoLitBert_v32k_cos1_2_50k - Polish RoBERTa model
No full textPolish RoBERTa model trained on Polish Wikipedia, Polish literature and Oscar
Cleaned Polish Oscar corpus (32M lines)
No full textCleaned Polish Oscar corpus (part: 32M lines, 3.35 GB). Data was prepared with a few cleaning heuristics:
- remove sentences shorter than
- remove non-polish sentences
- remove ungrammatical sentences
- perform sentence tokenization and save each sentence in a new line, after each document the new line was adde
Enriching plWordNet with morphology
Full text linkIn the paper, we present the process of adding morphological information to the Polish WordNet (plWordNet). We describe the reasons for this connection and the intuitions behind it. We also draw attention to the specificity of the Polish morphology. We show in which tasks the morphological information is important and how the methods can be developed by extending them to include combined morphological information based on WordNet
Korpus nagrań radiowych
No full textA collection of radio 192 recordings, with around 200 speakers, each no longer than 40 minutes long. Audio saved as RAW 16-bit 16 kHz sampling frequency
EU Parliament Speech corpus
No full textA collection of 1040 EU parliament speeches with transcription and annotations. Includes original speeches and PL/EN translations
Cleaned Polish Oscar corpus (64M lines)
No full textCleaned Polish Oscar corpus (part: 64M lines, 3.45 GB). Data was prepared with a few cleaning heuristics:
- remove sentences shorter than
- remove non-polish sentences
- remove ungrammatical sentences
- perform sentence tokenization and save each sentence in a new line, after each document the new line was adde
Discriminating Homonymy from Polysemy in Wordnets: English, Spanish and Polish Nouns
Full text linkWe propose a novel method of homonymy-polysemy discrimination for three Indo-European Languages (English, Spanish and Polish). Support vector machines and LASSO logistic regression were successfully used in this task, outperforming baselines. The feature set utilised lemma properties, gloss similarities, graph distances and polysemy patterns. The proposed ML models performed equally well for English and the other two languages (constituting testing data sets). The algorithms not only ruled out most cases of homonymy but also were efficacious in distinguishing between closer and indirect semantic relatedness
Metaphor annotations in Polish political debates from 2020 (TVP 2019-10-01 and TVN 2019-10-08) – presidential election
No full textThe data published here are a supplementary for a paper to be published in Metaphor and Social Words (under revision).
Two debates organised and published by TVP and TVN were transcribed and annotated with Metaphor Identification Method. We have used eMargin software (a collaborative textual annotation tool, (Kehoe and Gee 2013) and a slightly modified version of MIP (Pragglejaz 2007). Each lexical unit in the transcript was labelled as a metaphor related word (MRW) if its “contextual meaning was related to the more basic meaning by some form of similarity” (Steen 2007). The meanings were established with the Wielki Słownik Języka Polskiego (Great Dictionary of Polish, ed. (Żmigrodzki 2019). In addition to MRW, lexemes which create a metaphorical expression together with MRW were tagged as metaphor expression word (MEW). At least two words are needed to identify the actual metaphorical expression, since MRW cannot appear without MEW. Grammatical construction of the metaphor (Sullivan 2009) is asymmetrical: one word is conceptually autonomous and the other is conceptually dependent on the first. Within construction grammar terms (Langacker 2008), metaphor related word is elaborated with/by metaphorical expression word, because the basic meaning of MRW is elaborated and extended to more figurative meaning only if it is used jointly with MEW. Moreover, the meaning of the MEW is rather basic, concrete, as it remains unchanged in connection with the MRW. This can be clearly seen in the expression often used in our data: “Służba zdrowia jest w zapaści” (“Health service suffers from a collapse.”) where the word “zapaść” (“collapse”) is an example of MRW and words “służba zdrowia” (“health service”) are labeled as MEW. The English translation of this expression needs a different verb, instead of “jest w zapaści” (“is in collapse”) the English unmarked collocation is “suffers from a collapse”, therefore words “suffers from a collapse” are labeled as MRW. The “collapse” could be caused by heart failure, such as cardiac arrest or any other life-threatening medical condition and “health service” is portrayed as if it could literally suffer from such a condition – a collapse.
The data are in csv tables exported from xml files downloaded from eMargin site. Prior to annotation transcripts were divided to 40 parts, each for one annotator. MRW words are marked as MLN, MEW are marked as MLP and functional words within metaphorical expression are marked MLI, other words are marked just noana, which means no annotation needed
PoLitBert_v32k_tri_125k - Polish RoBERTa model
No full textPolish RoBERTa model trained on Polish Wikipedia, Polish literature and Oscar
