R/poster.Rmd

---
title: "News from the International Comparable Corpus"
subtitle: "First launch of ICC written"
date: "`r Sys.Date()`"
author:
    - name: Marc Kupietz
      affil: 1
    - name: Adrien Barbaresi
      affil: 2
    - name: Anna Cermakova
      affil: 3
    - name: Małgorzata Czachor
      affil: 4
    - name: Nils Diewald
      affil: 1
    - name: Jarle Ebeling
      affil: 5
    - name: Rafał L. Górski
      affil: 4
    - name: John Kirk
      affil: 6
    - name: Michal Křen
      affil: 3
    - name: Harald Lüngen
      affil: 1
    - name: Eliza Margaretha
      affil: 1
    - name: Signe Oksefjell Ebeling
      affil: 5
    - name: Mícheál Ó Meachair
      affil: 7
    - name: Ines Pisetta
      affil: 1
    - name: Elaine Uí Dhonnchadha
      affil: 8
    - name: Friedemann Vogel
      affil: 9
    - name: Rebecca Wilm
      affil: 1
    - name: Jiajin Xu
      affil: 10
    - name: Rameela Yaddehige
      affil: 1
affiliation:
  - num: 1
    address: IDS Mannheim
  - num: 2
    address: BBAW Berlin
  - num: 3
    address: Charles University
  - num: 4
    address: Polish Academy of Sciences
  - num: 5
    address: University of Oslo
  - num: 6
    address: University of Vienna
  - num: 7
    address: Dublin City University
  - num: 8
    address: Trinity College Dublin
  - num: 9
    address: University of Siegen
  - num: 10
    address: Beijing Foreign Studies University


logoleft_name: "../Figures/ICC_COL.svg"
author_textsize: "32pt"

output:
  posterdown::posterdown_html
---

```{r setup, include=FALSE, echo=FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = FALSE, warnings = FALSE)
source("common.R")
```
# ICC aims & charcteristics

* open initiative
* to improve the empirical basis for contrastive linguistics 
* by compiling comparable corpora for many languages 
* and making them as freely available as possible
* also by providing tools to query and analyse them
* mostly based on existing corpora
* mimics the composition of ICE

# Current alpha launch

## Composition of parts 
### By ICC genre

```{r composition_by_genre, message = FALSE, fig.width=14, fig.height=10, out.width = "100%"}
icc_genre <- icc %>%
  expand_grid(genre) %>%
  mutate(vc = paste0("iccGenre=", genre)) %>%
  rowwise() %>%
  mutate(tokens= corpusStats(icc_con(lang, token), vc = vc)@tokens)

icc_genre %>% ggplot(aes(x=lang, fill=genre, y=tokens)) +
  geom_col() + scale_y_continuous(labels = label_number(scale_cut = cut_short_scale())) +
  theme_ids(base_size = 24) +
  theme(
    axis.title.x = element_text(size = rel(1.5), face = "bold"),
    axis.title.y = element_text(size = rel(1.5), face = "bold"),
     axis.text = element_text(size = rel(0.70)),
    legend.title = element_text(size = rel(0.85), face = "bold"),
    legend.text = element_text(size = rel(1))) +
  scale_fill_ids() +
  geom_text(aes(label=if_else(tokens > 0, as.character(tokens), ""), y=tokens), position= position_stack(reverse = F, vjust = 0.5), color="black", size=6.2, family="Fira Sans Condensed")

```

### By date of publication


```{r composition_by_pubdate, message=F, warning=F, fig.width=14, fig.height=7, out.width = "100%"}
year <- c(1986:2023)

icc_year <- icc %>%
  expand_grid(year) %>%
  mutate(vc = paste0("pubDate in ", year)) %>%
  rowwise() %>%
  mutate(tokens= corpusStats(icc_con(lang, token), vc = vc)@tokens)

icc_year %>% ggplot(aes(x=year, fill=lang, color=lang, y=tokens)) +
  # geom_smooth(se=F, span=0.25) +
  xlim(1990, 2023) +
  ylim(0, NA) +
  stat_smooth(
        geom = 'area', method = 'loess', span = 1/4,
        alpha = 0.1) +
  # geom_area(alpha=0.1,  position = "identity") +
  scale_fill_ids() + scale_colour_ids() + 
  scale_y_continuous(labels = label_number(scale_cut = cut_short_scale())) +
  theme_ids(base_size=24) + 
    theme(
    axis.title.x = element_text(size = rel(1.5), face = "bold"),
    axis.title.y = element_text(size = rel(1.5), face = "bold"),
     axis.text = element_text(size = rel(1)),
    legend.title = element_text(size = rel(1), face = "bold"),
    legend.text = element_text(size = rel(1))) 
```

### Part-of-Speech proportions

```{r pos_proportions, fig.width=14, fig.height=10, out.width = "100%"}
POS_tag <- c(
  "ADJ", 	"ADP",#	"PUNCT",
  "ADV",	"AUX",	# "SYM",
  # "INTJ",
  "CCONJ", #	"X",
  "NOUN",	"DET",
  "PROPN",	#"NUM",
  "VERB",	#"PART",
  "PRON",
  "SCONJ"
  )

icc_by_pos_tag <- icc %>% expand_grid(POS = POS_tag) %>%
  rowwise() %>%
  mutate(f = frequencyQuery(icc_con(lang), sprintf("[ud/p=%s]", POS))$f)

icc_by_pos_tag %>% ggplot(aes(x=lang, fill = POS, y=f)) +
  geom_col() + scale_y_continuous(labels = label_number(scale_cut = cut_short_scale())) +
  scale_fill_ids() + scale_color_ids() +
  theme_ids(base_size=24) + 
    theme(
    axis.title.x = element_text(size = rel(1.5), face = "bold"),
    axis.title.y = element_text(size = rel(1.5), face = "bold"),
     axis.text = element_text(size = rel(1)),
    legend.title = element_text(size = rel(1), face = "bold"),
    legend.text = element_text(size = rel(1))) +
  geom_text(aes(label=sprintf("%.2f%%", 100*f), y=f), position= position_stack(reverse = F, vjust = 0.5), color="black", size=6.2, family="Fira Sans Condensed")
```

# Identification of Light Verb Constructions with *take*


## English: *take*

```{r take_icc, echo=TRUE, message=FALSE}
take_ca_icc <-
  collocationAnalysis(
    icc_con("eng"),
    "focus({[ud/l=take]} [ud/p=NOUN])",
    leftContextSize = 0,
    rightContextSize = 1,
    minOccur = 2,
    addExamples = T
  )

take_ca_icc %>% show_table()
```