Convert Transliterated Sumerian Text to Cuneiform

Description

Converts transliterated Sumerian text to Unicode cuneiform characters. This is a generic function with a method for character vectors.

Usage

as.cuneiform(x, ...)

## Default S3 method:
as.cuneiform(x, ...)

## S3 method for class 'character'
as.cuneiform(x, mapping = NULL, ...)

## S3 method for class 'cuneiform'
print(x, ...)

Arguments

Details

The function processes each element of the input character vector by:

1. Calling info to look up sign information for each transliterated sign.

2. Extracting the Unicode cuneiform symbols for each sign.

3. Reconstructing the cuneiform text using the original separators, but removing hyphens and periods which are only used in transliteration to indicate sign boundaries.

The default method throws an error for unsupported input types.

Value

as.cuneiform returns a character vector of class cuneiform with the cuneiform representation of each input element.

print.cuneiform displays a character vector of class cuneiform.

Note

The cuneiform output requires a font that supports the Unicode Cuneiform block (U+12000 to U+12500) to display correctly.

See Also

info for retrieving detailed sign information, split_sumerian for splitting Sumerian text into signs, as.sign_name for converting transliterated Sumerian text intos sign names

Examples

# Convert transliterated text to cuneiform
as.cuneiform(c("na-an-jic li-ic ma","en tarah-an-na-ke4"))

# Load transliterated text from a file
file <- system.file("extdata", "transliterated-text.txt", package = "sumer")
x <- readLines(file)
cat(x, sep="\n")

# Convert transliterated text to cuneiform
as.cuneiform(x)

# Using a custom mapping table
path <- system.file("extdata", "etcsl_mapping.txt", package = "sumer")
my_mapping <- read.csv2(path, sep=";", na.strings="")
as.cuneiform("lugal", mapping = my_mapping)

Convert Transliterated Sumerian Text to Sign Names

Description

Converts transliterated Sumerian text to canonical sign names in uppercase notation. This is a generic function with a method for character vectors.

Usage

as.sign_name(x, ...)

## Default S3 method:
as.sign_name(x, ...)

## S3 method for class 'character'
as.sign_name(x, mapping = NULL, ...)

## S3 method for class 'sign_name'
print(x, ...)

Arguments

Details

The function processes each element of the input character vector by:

1. Calling info to look up sign information for each transliterated sign.

2. Extracting the canonical sign names for each sign.

3. Reconstructing the text using the original separators, but replacing hyphens with periods to follow standard sign name notation.

The default method throws an error for unsupported input types.

Value

as.sign_name returns a character vector of class c("sign_name", "character") with the sign name representation of each input element.

print.sign_name displays a character vector of class "sign_name".

See Also

as.cuneiform for converting to cuneiform characters, info for retrieving detailed sign information, split_sumerian for splitting Sumerian text into signs

Examples

# Convert transliterated text to sign names
as.sign_name(c("lugal-e", "an-ki"))

# Load transliterated text from a file
file <- system.file("extdata", "transliterated-text.txt", package = "sumer")
x <- readLines(file)
cat(x, sep="\n")

# Convert transliterated text to sign names
as.sign_name(x)

# Using a custom mapping table
path <- system.file("extdata", "etcsl_mapping.txt", package = "sumer")
my_mapping <- read.csv2(path, sep=";", na.strings="")
as.sign_name("lugal", mapping = my_mapping)

Convert Translation Data to a Sumerian Dictionary

Description

Converts a data frame of Sumerian translations into a structured dictionary format, adding cuneiform representations and phonetic readings for each sign.

Usage

convert_to_dictionary(df, mapping = NULL)

Arguments

Details

Processing Steps

1. Aggregates translations and counts occurrences of each unique combination in df

2. Looks up phonetic readings and cuneiform signs for each sign component

3. Combines cuneiform, reading, and translation rows into a single data frame

4. Sorts the result by sign name and row type

Reading Format

Phonetic readings are formatted as follows:

• Multiple possible readings are enclosed in braces: {a, dur5, duru5}

• For compound signs, readings of individual components are joined with hyphens

• If a sign has more than three possible readings in a compound, only the first three are shown followed by ...

• Unknown readings are marked with ?

Value

A data frame with the following columns:

sign_name

The normalized Sumerian text (e.g., "A", "AN", "A2.TAB")

row_type

Type of entry: "cunei." (cuneiform character), "reading" (phonetic readings), or "trans." (translation)

count

Number of occurrences for translations; NA for cuneiform and reading entries

type

Grammatical type (e.g., "S", "V", "A") for translations; empty string for other row types

meaning

The cuneiform character(s), phonetic reading(s), or translated meaning depending on row_type

The data frame is sorted by sign_name, row_type, and descending count.

See Also

read_translated_text for reading translation files, make_dictionary for creating a complete dictionary with cuneiform representations and readings in a single step.

Examples

# Read translations from a single text document
filename     <- system.file("extdata", "text_with_translations.txt", package = "sumer")
translations <- read_translated_text(filename)

# View the structure
head(translations)

#Make some custom unifications (here: removing the word "the")
translations$meaning <- gsub("\\bthe\\b", "", translations$meaning, ignore.case = TRUE)
translations$meaning <- trimws(gsub("\\s+", " ", translations$meaning))

# View the structure
head(translations)

#Convert the result into a dictionary
dictionary   <- convert_to_dictionary(translations)

# View the structure
head(dictionary)

# View entries for a specific sign
dictionary[dictionary$sign_name == "EN", ]

# With custom mapping
path  <- system.file("extdata", "etcsl_mapping.txt", package = "sumer")
mapping <- read.csv2(path, sep=";", na.strings="")
translations <- read_translated_text(filename, mapping = mapping)
dictionary <- convert_to_dictionary(translations, mapping = mapping)
head(dictionary)

Extract Hierarchical Skeleton Entries from Bracketed Text

Description

Recursively extracts the contents of nested round brackets from a normalized Sumerian text string and returns them as a data frame with position, length, nesting depth, and expression for each entry.

This is an internal helper function used by skeleton.

Usage

extract_skeleton_entries(x)

Arguments

Details

The function first extracts the contents of all outermost (top-level) bracket pairs using an internal helper function. For each extracted group, a row is added to the result data frame. If a group itself contains further nested brackets, the function recurses into it to extract deeper levels.

The depth value of each entry reflects the nesting level: entries from the outermost brackets have depth 1, entries nested one level deeper have depth 2, and so on.

The start column records the position (in tokens) of the first token in each group, relative to the full input. The n_tokens column gives the number of tokens in the group as determined by split_sumerian.

Value

A data frame with one row per extracted entry and the following columns:

If the input contains no brackets, an empty data frame with the same columns is returned.

See Also

skeleton which calls this function, mark_skeleton_entries for the preceding normalization step, split_sumerian for determining the number of tokens

Examples

# First normalize the input with mark_skeleton_entries
x <- "<d-nu-dim2-mud> ki a. jal2 (e2{kur}) ra. gaba jal2. an ki a"
normalized <- sumer:::mark_skeleton_entries(x)
normalized

# Then extract the hierarchical structure
sumer:::extract_skeleton_entries(normalized)

Posterior Probabilities of Grammatical Types for Each Sign

Description

For each cuneiform sign in a sentence, computes Bayesian posterior probabilities for all grammatical types, combining prior beliefs from prior_probs with observed dictionary frequencies. The dictionary counts are corrected for verb underrepresentation using the sentence_prob stored in the prior.

Usage

grammar_probs(sg, prior, dic, alpha0 = 1)

Arguments

Details

For each sign at position i in the sentence, the function computes:

1. The raw dictionary counts n_k for each grammar type k.

2. A correction factor x_k = 1 / \mathrm{sentence\_prob} for verb-like types, x_k = 1 otherwise. The corrected counts are m_k = n_k \cdot x_k with total M = \sum_k m_k.

3. The posterior probability (Dirichlet-Multinomial model):

   \theta_k = \frac{\alpha_0 \, p_k + m_k}{\alpha_0 + M}

   where p_k is the prior probability from prior_probs().

For signs not in the dictionary (M = 0), the posterior equals the prior. For signs with many observations (M \gg \alpha_0), the posterior is dominated by the data.

Value

A data frame with columns:

position

Integer. Position of the sign in the sentence.

sign_name

Character. The sign name.

cuneiform

Character. The cuneiform character.

type

Character. The grammar type (e.g., "S", "V", "Sx->S").

prob

Numeric. Posterior probability for this type at this position.

n

Numeric. Number of counts in the dictionary.

See Also

prior_probs for computing the prior, sign_grammar for the input data, plot_sign_grammar for visualisation.

Examples

dic   <- read_dictionary()
sg    <- sign_grammar("a-ma-ru ba-ur3 ra", dic)
prior <- prior_probs(dic, sentence_prob = 0.25)
gp    <- grammar_probs(sg, prior, dic, alpha0 = 1)
print(gp)

Look Up Translations for All Substrings of a Sumerian Text

Description

Converts a Sumerian text string into cuneiform tokens, generates all contiguous substrings, and looks up the most frequent translation for each substring in one or more dictionaries.

Usage

guess_substr_info(x, dic, mapping = NULL)

Arguments

Details

The function performs the following steps:

1. If dic is a character vector of file paths, the dictionaries are loaded with read_dictionary. If dic is a single data frame, it is wrapped in a list.

2. The input string x is converted to cuneiform with as.cuneiform and split into individual tokens with split_sumerian.

3. A data frame of all contiguous substrings is created with init_substr_info.

4. A sign_name column is added by converting each substring expression with as.sign_name.

5. For each substring, the dictionaries are searched in order. The most frequent translation (highest count among rows with row_type == "trans.") from the first dictionary that contains a match is used to fill in the type and translation columns.

Value

A data frame with one row per substring and the following columns:

The rows are ordered as in init_substr_info (by n_tokens descending, then start ascending), so that row indices can be computed with substr_position.

See Also

init_substr_info for creating the substring data frame, substr_position for computing row indices, read_dictionary for loading dictionaries, look_up for interactive dictionary lookup, skeleton for creating translation templates

Examples

# Load the built-in dictionary
dic <- read_dictionary()

# Look up translations for all substrings
x <- "lugal kur-ra-ke4"
df <- guess_substr_info(x, dic)

# Show rows that have a translation
df[df$translation != "", ]

# Use multiple dictionaries (ordered by reliability -> first match wins)
file1 <- system.file("extdata", "sumer-dictionary.txt", package = "sumer")
df <- guess_substr_info(x, file1)

Retrieve Information About Sumerian Signs

Description

Analyzes a transliterated Sumerian text string and retrieves detailed information about each sign, including syllabic readings, sign names, cuneiform symbols, and alternative readings.

The function info computes the result and returns an object of class "info". The print method displays a summary of different text representations in the console.

Usage

info(x, mapping = NULL)

## S3 method for class 'info'
print(x, flatten = FALSE, ...)

Arguments

Details

The function info performs the following steps:

1. Splits the input string into signs and separators using split_sumerian.

2. Standardizes the signs.

3. Looks up each sign in the mapping table based on its type:

   • Type 1 (lowercase): Searches for a matching syllable reading.

   • Type 2 (uppercase): Searches for a matching sign name.

   • Type 3 (cuneiform): Searches for a matching cuneiform character.

4. Returns a data frame with the results, along with the separators stored as an attribute.

The mapping table must contain the following columns:

syllables

Comma-separated list of possible syllabic readings for the sign. The first reading is used as the default.

name

The canonical sign name in uppercase.

cuneiform

The Unicode cuneiform character.

The print method displays each sign with its name and alternative readings, followed by three text representations: syllables, sign names, and cuneiform text.

Value

info returns a data frame of class c("info", "data.frame") with one row per sign and the following columns:

The data frame has an attribute "separators" containing the separator characters between signs.

print.info prints the following to the console and returns x invisibly:

Sign table

Each sign with its cuneiform symbol, name, and alternative readings.

syllables

The text with syllabic readings, using hyphens as separators within words.

sign names

The text with sign names, using periods as separators within words.

cuneiform text

The text rendered in Unicode cuneiform characters, with hyphens and periods removed.

Note

If no custom mapping is provided, the function loads the internal mapping file included with the sumer package.

See Also

split_sumerian for splitting Sumerian text into signs,

Examples

library(stringr)

# Basic usage - compute and print
info("lugal-e")

# Store the result for further processing
result <- info("an-ki")
result

# Access the underlying data frame
result$sign
result$name

# Print with and without flattened separators
result <- info("(an)na")
print(result)
print(result, flatten = TRUE)

# Using a custom mapping table
path <- system.file("extdata", "etcsl_mapping.txt", package = "sumer")
my_mapping <- read.csv2(path, sep=";", na.strings="")
info("an-ki", mapping = my_mapping)

Initialize a Data Frame of All Substrings

Description

Creates a data frame containing all contiguous substrings of a token vector, including the full token sequence itself. Each row represents one substring, with its starting position, length in tokens, the concatenated expression, and empty columns for type and translation.

The rows are ordered by n_tokens descending and start ascending, so that the row number can be computed from start and n_tokens using substr_position.

This is an internal helper function.

Usage

init_substr_info(token)

Arguments

Details

For a token vector of length N, the function generates all N(N+1)/2 contiguous substrings. The substrings are ordered by n_tokens descending (longest first) and within each group by start ascending. This ordering ensures that the row index of any substring can be computed with the formula

\mathrm{row} = \frac{(N - k)(N - k + 1)}{2} + s

where k is the number of tokens (n_tokens) and s is the starting position (start).

The expr column contains the tokens concatenated without separators. The type and translation columns are initialized as empty strings, intended to be filled in later.

Value

A data frame with N(N+1)/2 rows and the following columns:

See Also

substr_position for computing the row index from start and n_tokens, skeleton for creating translation templates, make_dictionary for creating dictionaries from filled-in templates

Examples

x<-"<d-nu-dim2-mud> ki a. jal2 (e2{kur}) ra. gaba jal2. an ki a"

token <- split_sumerian(as.cuneiform(x))$signs

df <- sumer:::init_substr_info(token)
df

# Verify that substr_position recovers the row indices
N <- length(token)
all(seq_len(nrow(df)) == sumer:::substr_position(df$start, df$n_tokens, N))

Look Up Sumerian Signs or Search for Translations

Description

Searches a Sumerian dictionary either by sign name (forward lookup) or by translation text (reverse lookup).

The function look_up computes the search results and returns an object of class "look_up". The print method displays formatted results with cuneiform representations, grammatical types, and translation counts.

Usage

look_up(x, dic, lang = "sumer", width = 70, mapping = NULL)

## S3 method for class 'look_up'
print(x, ...)

Arguments

Details

Search Modes

The function operates in two modes depending on the input:

Forward Lookup (Sumerian input detected):

1. Converts the sign name to cuneiform

2. Retrieves all translations for the exact sign combination

3. Retrieves translations for all individual signs and substrings

Reverse Lookup (non-Sumerian input):

1. Searches for the term in all translation meanings

2. Retrieves matching entries with sign names and cuneiform

Output Format

The print method displays results with:

• Sign names with cuneiform representations

• Occurrence counts in brackets (e.g., [29])

• Grammatical type abbreviations (e.g., S, V)

• Translation meanings with automatic line wrapping

• Search term highlighting in blue for reverse lookups (only for ANSI-compatible terminals)

Value

look_up returns an object of class "look_up", which is a list containing:

print.look_up prints formatted dictionary entries to the console and returns x invisibly.

See Also

read_dictionary for loading dictionaries, make_dictionary for creating dictionaries, as.cuneiform for cuneiform conversion.

Examples

# Load dictionary
dic <- read_dictionary()

# Forward lookup: search by phonetic spelling
look_up("d-suen", dic)

# Forward lookup: search by Sumerian sign name
look_up("AN", dic)
look_up("AN.EN.ZU", dic)

# Forward lookup: search by cuneiform character string
AN.NA <- paste0(intToUtf8(0x1202D), intToUtf8(0x1223E))
AN.NA
look_up(AN.NA, dic)

# Reverse lookup: search in translations
look_up("Gilgamesh", dic, "en")

# Adjust output width for narrow terminals
look_up("water", dic, "en", width = 50)

# Store results for later use
result <- look_up("lugal", dic)
result$cuneiform
result$translations

# Print stored results
print(result)

Create a Sumerian Dictionary from Annotated Text Files

Description

Parses Word documents (.docx) or plain text files containing annotated Sumerian translations and creates a structured dictionary data frame. The function extracts sign names, their cuneiform representations, possible readings, and translations with grammatical types.

Usage

make_dictionary(file, mapping = NULL)

Arguments

Details

Input Format

The input files must contain lines starting with | in the following format:

|sign_name: TYPE: meaning

or

|equation for sign_name: TYPE: meaning

For example:

|a2-tab: S: the double amount of work performance
|me=ME: S: divine force
|AN: S: god of heaven
|na=NA: Sx->A: whose existence is bound to S

Lines not starting with | are ignored. Only the first entry in an equation of sign names is used for the dictionary. The following notation is suggested for grammatical types:

• S for substantives and noun phrases, (e.g., "the old man in the temple")

• V for verbs and decorated verbs (e.g., "to go", "to bring the delivery into the temple")

• A for adjectives, attributes and subordinate clauses that further define the subject (e.g., "who/which is weak", "whose resource for sustaining life is grain")

• Sx->A for a symbol that transforms the preceding noun phrase into an attribute (e.g., "whose resource for sustaining life is S"). Other transformations are denoted accordingly.

• N for numbers,

• D for everything else.

Processing Steps

1. Extracts text from .docx files or reads plain text

2. Filters lines starting with |

3. Normalizes sign names and looks up possible readings from the mapping table

4. Aggregates translations and counts occurrences

Output Structure

For each unique sign, the output contains:

• One cunei. row with the cuneiform character(s)

• One reading row with possible phonetic readings

• One or more trans. rows with translations, sorted by frequency

Value

A data frame with the following columns:

sign_name

The normalized Sumerian sign name (e.g., "A", "AN", "ME")

row_type

Type of entry: "cunei." (cuneiform), "reading" (phonetic readings), or "trans." (translation)

count

Number of occurrences for translations; NA for cuneiform and reading entries

type

Grammatical type (e.g., "S", "V", "Sx->A") for translations; empty for other line types

meaning

The cuneiform character(s), reading(s), or translated meaning depending on line_type

See Also

as.cuneiform, split_sumerian

Examples

# Create a dictionary from a single text document
filename  <- system.file("extdata", "text_with_translations.txt", package = "sumer")
dict <- make_dictionary(filename)

# Use the dictionary
look_up("an", dict)

Mark N-gram Combinations in Cuneiform Text

Description

Takes a character vector of Sumerian text and marks all n-gram combinations (from ngram_frequencies) with curly braces. Longer combinations are marked first, shorter ones afterwards (including inside already-marked regions).

Usage

mark_ngrams(x, ngram, mapping = NULL)

Arguments

Details

The function first converts x to cuneiform (if not already) and removes spaces and brackets ()[]{}.

Then it sorts ngram descending by length and replaces each occurrence of a combination with {combination} (space, open brace, combination, close brace, space).

Shorter n-grams may be marked inside already-marked longer n-grams (nesting is allowed).

Value

A character vector of cuneiform text with n-gram combinations enclosed in curly braces and surrounded by spaces.

See Also

ngram_frequencies

Examples

# Load the example text of "Enki and the World Order"
path  <- system.file("extdata", "enki_and_the_world_order.txt", package = "sumer")
text <- readLines(path, encoding="UTF-8")
cat(text[1:10],sep="\n")

# Find combinations that appear at least 6 times in the text
freq <- ngram_frequencies(text, min_freq = 6)
freq[1:10,]

# Mark these combinations in the text
text_marked <- mark_ngrams(text, freq)
cat(text_marked[1:10], sep="\n")

# You can enter transliterated text
x <- "kij2-sig unu2 gal d-re-e-ne-ka me-te-ac im-mi-ib-jal2"
mark_ngrams(x, freq)

# Find all occurences of a pattern in the annotated text
term     <- "IGI.DIB.TU"
(pattern <- mark_ngrams(term, freq))
result   <- text_marked[grepl(pattern, text_marked, fixed=TRUE)]
cat(result, sep="\n")

Normalize Brackets for Skeleton Generation

Description

Transforms a transliterated Sumerian text string into a normalized form that contains only round brackets. This prepares the input for hierarchical extraction by extract_skeleton_entries.

This is an internal helper function used by skeleton.

Usage

mark_skeleton_entries(x)

Arguments

Details

The function performs the following transformations:

1. Tokenizes the input using an internal helper function. Tokens enclosed in angle brackets are merged into a single token.

2. Removes all angle brackets and curly braces from the separators, replacing them with spaces.

3. Wraps every token that is not already enclosed in round brackets with round brackets.

The result is a string in which every token is enclosed in round brackets. Existing round brackets from the input are preserved, so the nesting structure reflects the grouping specified in the original input.

For example, the input

"<d-nu-dim2-mud> ki a. jal2 (e2{kur}) ra"

is transformed into a string where d-nu-dim2-mud appears as a single bracketed token, e2 and kur are individually bracketed inside the existing round brackets around e2{kur}, and all other tokens (ki, a, jal2, ra) are each wrapped in their own round brackets.

Value

A character string of length 1 in which all tokens are enclosed in round brackets. The string contains only round brackets as grouping characters (no angle brackets or curly braces).

See Also

skeleton which calls this function, extract_skeleton_entries for the subsequent extraction step, split_sumerian for the underlying sign-splitting logic

Examples

# Input with all three bracket types
x <- "<d-nu-dim2-mud> ki a. jal2 (e2{kur}) ra. gaba jal2. an ki a"
sumer:::mark_skeleton_entries(x)

# Input without any brackets: each token gets wrapped in round brackets
sumer:::mark_skeleton_entries("LUGAL.E")

# Angle brackets merge tokens into a single unit
sumer:::mark_skeleton_entries("<an-ki> lugal")

Frequency Analysis of Cuneiform Sign Combinations (N-grams)

Description

Analyzes a Sumerian text for frequently occurring cuneiform sign combinations (n-grams). The input can be either cuneiform text or transliterated text (which is automatically converted to cuneiform via as.cuneiform). The analysis starts with the longest combinations and works down to single signs, masking already-counted occurrences to avoid reporting subsequences that are only frequent because they are part of a longer frequent combination. N-grams are searched within lines only (not across line boundaries).

Usage

ngram_frequencies(x, min_freq = c(6, 4, 2), mapping = NULL)

Arguments

Details

A “sign” is defined as either a single cuneiform Unicode character (U+12000 to U+1254F) or a character sequence enclosed in mathematical angle brackets (U+27E8 ... U+27E9), which is treated as a single token. All other characters (spaces, X, numbers, punctuation, etc.) are skipped during tokenization.

The maximum n-gram length is automatically determined as the length of the longest tokenized line in the input.

The analysis proceeds from the longest combinations down to single signs. When a combination is identified as frequent (i.e., meets the minimum frequency threshold), all occurrences except the first are masked before continuing with shorter combinations. This prevents subsequences from being reported as frequent when their frequency is solely due to a longer frequent combination.

Value

A data frame with three columns, sorted by descending length, then descending frequency:

See Also

as.sign_name for converting cuneiform to sign names, as.cuneiform for converting transliterations to cuneiform, split_sumerian for tokenizing transliterated text.

Examples

# Read the text "Enki and the World Order"

path  <- system.file("extdata", "enki_and_the_world_order.txt", package = "sumer")
text <- readLines(path, encoding="UTF-8")

cat(text[1:10],sep="\n")

# Find combinations that appear at least 6 times in the text
freq <- ngram_frequencies(text, min_freq = 6)

freq[1:10,]

Stacked Bar Chart of Grammatical Type Frequencies

Description

Creates a stacked bar chart from the output of sign_grammar or grammar_probs. Each bar represents one sign position in the sentence. The colours indicate the relative frequency or posterior probability of each individual grammatical type.

Usage

plot_sign_grammar(sg,
                  output_file = NULL,
                  width       = 10,
                  height      = 5,
                  sign_names  = FALSE,
                  font_family = NULL,
                  mapping     = NULL)

Arguments

Details

When the input comes from sign_grammar() (column n), absolute frequencies are converted to percentages so that bars sum to 100%. When the input comes from grammar_probs() (column prob), posterior probabilities are used directly.

Colours are assigned per grammatical type, grouped by class:

• Red shades: Verbs (V) and operators returning verbs

• Blue shades: Operators returning attributes A

• Orange: Adjectives and other signs with grammatical type (Sx->S)

• Green: Nouns

• Grey/other shades: All other types

Value

Invisibly returns the ggplot2 plot object.

See Also

sign_grammar for generating raw frequency data, grammar_probs for Bayesian posterior probabilities, prior_probs for computing the prior.

Examples

dic   <- read_dictionary()
sg    <- sign_grammar("a-ma-ru ba-ur3 ra", dic)

# Plot raw frequencies
file <- file.path(tempdir(), "test.png")
plot_sign_grammar(sg, file)

# Plot probabilities
prior <- prior_probs(dic, sentence_prob = 0.25)
gp    <- grammar_probs(sg, prior, dic, alpha0 = 1)
file  <- file.path(tempdir(), "test2.png")
plot_sign_grammar(gp, file)

Prior Probabilities of Grammatical Types

Description

Computes prior probabilities for each grammatical type (e.g., S, V, Sx->S, xS->A, etc.) from a dictionary. The priors can be corrected for verb underrepresentation in the dictionary data.

Usage

prior_probs(dic, sentence_prob = 1.0)

Arguments

Details

The function proceeds in three steps:

1. For each single-sign dictionary entry with at least one count, the counts per grammatical type are normalised to sum to 1.

2. The prior probability of each type is the mean of these normalised frequencies across all signs.

3. A correction is applied: counts of verb-like types (V and all operators with return type V, such as Vx->V or xV->V) are multiplied by 1/sentence_prob, then all probabilities are renormalised. This compensates for the fact that verbs are underrepresented when most dictionary entries are obtained from noun phrases rather than complete sentences.

When sentence_prob = 1, no correction is applied.

Value

A named numeric vector with one element per grammatical type found in the dictionary, summing to 1. The names are the type strings as they appear in the dictionary (e.g., "S", "V", "Sx->S"). The sentence_prob parameter is stored as an attribute.

See Also

sign_grammar for per-sign grammatical type frequencies.

Examples

dic   <- read_dictionary()

# Default usage
prior_probs(dic)

# Applying correction (only 25% sentences in training data)
prior_probs(dic, sentence_prob = 0.25)

Read a Sumerian Dictionary from File

Description

Reads a Sumerian dictionary from a semicolon-separated text file, optionally displaying the metadata header with author, version, and update information.

Usage

read_dictionary(file = NULL, verbose = TRUE)

Arguments

Details

File Format

The function expects a semicolon-separated file with a metadata header. Lines starting with # are treated as comments. The expected format is:

###---------------------------------------------------------------
###                Sumerian Dictionary
###
### Author:  Robin Wellmann
### Year:    2026
### Version: 0.5
### Watch for Updates:
###   https://founder-hypothesis.com/en/sumerian-mythology/downloads/
###---------------------------------------------------------------
sign_name;row_type;count;type;meaning
A;cunei.;;;<here would be the cuneiform sign for A>
A;reading;;;{a, dur5, duru5}
A;trans.;3;S;water

Encoding

The file is read with UTF-8 encoding to properly handle cuneiform characters.

Value

A data frame with the following columns:

sign_name

The Sumerian sign name (e.g., "A", "AN", "ME")

row_type

Type of entry: "cunei." (cuneiform character), "reading" (phonetic readings), or "trans." (translation)

count

Number of occurrences for translations; NA for cuneiform and reading entries

type

Grammatical type (e.g., "S", "V") for translations; empty string for other row types

meaning

The cuneiform character(s), phonetic reading(s), or translated meaning depending on row_type

See Also

save_dictionary for saving dictionaries to file, make_dictionary and convert_to_dictionary for creating dictionaries.

Examples

# Load the built-in dictionary
dic <- read_dictionary()

# Load a custom dictionary
filename <- system.file("extdata", "sumer-dictionary.txt", package = "sumer")
dic <- read_dictionary(filename)

# Look up an entry
look_up("d-suen", dic)

Read Annotated Sumerian Translations from Text Files

Description

Reads Word documents (.docx) or plain text files containing annotated Sumerian translations and extracts sign names, grammatical types, and meanings into a structured data frame.

Usage

read_translated_text(file, mapping=NULL)

Arguments

Details

Input Format

The input files must contain lines starting with | in the following format:

|sign_name: TYPE: meaning

or

|equation for sign_name: TYPE: meaning

For example:

|a2-tab: S: the double amount of work performance
|me=ME: S: divine force
|AN: S: god of heaven
|na=NA: Sx->A: whose existence is bound to S

Lines not starting with | are ignored. Only the first entry in an equation of sign names is extracted. The following notation is suggested for grammatical types:

• S for substantives and noun phrases, (e.g., "the old man in the temple")

• V for verbs and decorated verbs (e.g., "to go", "to bring the delivery into the temple")

• A for adjectives, attributes and subordinate clauses that further define the subject (e.g., "who/which is weak", "whose resource for sustaining life is grain")

• Sx->A for a symbol that transforms the preceding noun phrase into an attribute (e.g., "whose resource for sustaining life is S"). Other transformations are denoted accordingly.

• N for numbers,

• D for everything else.

Processing Steps

1. Reads text from .docx files or plain text files

2. Filters lines starting with |

3. Parses each line into sign name, type, and meaning components

4. Normalizes transliterated text by removing separators and looking up the sign names from the mapping

5. Cleans meaning field by removing content after ; or | delimiters

6. Issues a warning for entries with missing type annotations

7. Excludes empty sign names from the result

Value

A data frame with the following columns:

sign_name

The normalized sign name with components separated by hyphens (e.g., "A", "AN", "X-NA")

type

Grammatical type (e.g., "S", "V", "A", "Sx->A")

meaning

The translated meaning of the sign

Note

If any translations have missing type annotations, the function prints a warning message listing the affected entries.

See Also

convert_to_dictionary for converting the result into a dictionary, make_dictionary for creating a complete dictionary with cuneiform representations and readings in a single step.

Examples

# Read translations from a single text document
filename     <- system.file("extdata", "text_with_translations.txt", package = "sumer")
translations <- read_translated_text(filename)

# View the structure
head(translations)

# Filter by grammatical type
nouns <- translations[translations$type == "S", ]
nouns

#Make some custom unifications (here: removing the word "the")
translations$meaning <- gsub("\\bthe\\b", "", translations$meaning, ignore.case = TRUE)
translations$meaning <- trimws(gsub("\\s+", " ", translations$meaning))

# View the structure
head(translations)

#Convert the result into a dictionary
dictionary   <- convert_to_dictionary(translations)

# View the structure
head(dictionary)

Save a Sumerian Dictionary to File

Description

Saves a Sumerian dictionary data frame to a semicolon-separated text file with a metadata header containing author, year, version, and URL information.

Usage

save_dictionary(dic, file, author = "", year = "", version = "", url = "")

Arguments

Details

Output Format

The output file consists of two parts:

1. A metadata header with lines starting with ###, containing author, year, version, and URL information

2. The dictionary data in semicolon-separated format with columns: sign_name, row_type, count, type, meaning

Example output:

###---------------------------------------------------------------
###                Sumerian Dictionary
###
### Author:  Robin Wellmann
### Year:    2026
### Version: 1.0
### Watch for Updates: https://founder-hypothesis.com/sumer/
###---------------------------------------------------------------
sign_name;row_type;count;type;meaning
A;cunei.;;;<cuneiform sign for A>
A;reading;;;{a, dur5, duru5}
A;trans.;3;S;water

Value

No return value. The function is called for its side effect of writing the dictionary to a file.

See Also

make_dictionary and convert_to_dictionary for creating dictionaries, read_dictionary for reading saved dictionaries.

Examples

# Create and save a dictionary

filename  <- system.file("extdata", "text_with_translations.txt", package = "sumer")
dictionary <- make_dictionary(filename)

save_dictionary(
  dic     = dictionary,
  file    = file.path(tempdir(), "sumerian_dictionary.txt"),
  author  = "John Doe",
  year    = "2026",
  version = "1.0",
  url     = "https://example.com/dictionary"
)

Grammatical Type Frequencies for Each Sign in a Sumerian Sentence

Description

For each cuneiform sign in a Sumerian sentence, looks up the dictionary to determine the frequency of each individual grammatical type (e.g., S, V, Sx->S, xS->A). Returns a data frame with one row per sign per grammatical type.

Usage

sign_grammar(x, dic, mapping = NULL)

Arguments

Details

The function converts the input to cuneiform, splits it into individual signs, and looks up each sign in the dictionary. For each sign, the translations are grouped by their individual type string (e.g., "S", "V", "Sx->S", "xS->A").

For each type the dictionary count values are summed. If a translation entry has no count, it is treated as 1.

The set of types returned is the union of all types found across all signs in the sentence. Each sign gets one row per type, even if the count is 0 for that type.

Value

A data frame with columns:

position

Integer. Position of the sign in the sentence.

sign_name

Character. The sign name (e.g., "KA").

cuneiform

Character. The cuneiform character.

type

Character. The grammar type string (e.g., "S", "V", "Sx->S").

n

Integer. Sum of dictionary counts for this sign and this type.

See Also

grammar_probs for Bayesian posterior probabilities, plot_sign_grammar for visualising the result, read_dictionary for loading a dictionary, as.cuneiform for cuneiform conversion.

Examples

dic <- read_dictionary()

# Analyse a sentence
sg <- sign_grammar("a-ma-ru ba-ur3 ra", dic)
print(sg)

# Use with cuneiform input
x<-"\U00012000\U000121AD"
print(x)
sg <- sign_grammar(x, dic)
print(sg)

Create a Translation Template for Sumerian Text

Description

Creates a structured template (skeleton) for translating Sumerian text. The template displays each token and subexpression with its syllabic reading, sign name, and cuneiform representation, providing a framework for adding translations.

The input may contain three types of brackets to control how the template is generated (see Details). Optionally, the template can be pre-filled with translations from one or more dictionaries using guess_substr_info.

The function skeleton computes the template and returns an object of class "skeleton". The print method displays the template in the console.

Usage

skeleton(x, mapping = NULL, fill = NULL, space = FALSE)

## S3 method for class 'skeleton'
print(x, ...)

Arguments

Details

The function generates a hierarchical template from a Sumerian text string. The input is first converted to cuneiform with as.cuneiform. The input string may contain three types of brackets that control how entries in the template are generated:

Angle brackets < >

The enclosed token sequence is treated as a fixed term. No individual skeleton entries are generated for the tokens inside. For example, <d-nu-dim2-mud> is treated as a single unit.

Round brackets ( )

The enclosed token sequence is a coherent term for which a single skeleton entry is generated, in addition to entries for its individual tokens. Nesting is allowed.

Curly braces { }

Ignored during skeleton generation. They can be used in the input to indicate which tokens serve as arguments to an operator, but this information is not needed for the skeleton.

In addition, a skeleton entry is generated for every individual token that does not appear inside angle brackets.

Each line in the resulting template follows the format:

|[tabs]reading=SIGN.NAME=cuneiform:type:translation

When fill is not provided, the type and translation fields are left empty:

|[tabs]reading=SIGN.NAME=cuneiform::

The template should then be filled in as follows:

• Between the two colons: the grammatical type of the expression (e.g., S for noun phrases, V for verbs). See make_dictionary for details.

• After the second colon: the translation.

The indentation level (number of tabs) reflects the nesting depth: top-level entries have no indentation, their sub-entries have one tab, and so on.

The template format is designed to be saved as a text file (.txt) or Word document (.docx), edited manually, and then used as input for make_dictionary to create a custom dictionary.

If fill is provided, the function validates that fill matches x: the cuneiform tokens of the first row in fill must be identical to the tokens of x, and the number of rows must equal N(N+1)/2 where N is the number of tokens.

Value

skeleton returns a character vector of class c("skeleton", "character") containing the template lines. The first line is the header with the full reading of the input, followed by one line per skeleton entry. If space = TRUE, empty strings are inserted as separator lines.

print.skeleton prints the template to the console (one line per element) and returns x invisibly.

See Also

guess_substr_info for generating the fill data frame, mark_skeleton_entries for the bracket normalization step, extract_skeleton_entries for the hierarchical extraction step, substr_position for computing row indices in the fill data frame, look_up for looking up translations of Sumerian signs and words, make_dictionary for creating a dictionary from filled-in templates, info for retrieving detailed sign information

Examples

# Create an empty template
x <- "<d-nu-dim2-mud> ki a. jal2 (e2{kur}) ra. gaba jal2. an ki a"
skeleton(x)

# Pre-fill the template with dictionary translations
dic <- read_dictionary()
fill <- guess_substr_info(x, dic)
skeleton(x, fill = fill)

# Use spacing to visually separate top-level groups
skeleton(x, fill = fill, space = TRUE)

Split a String into Sumerian Signs and Separators

Description

Splits a transliterated Sumerian text string into its constituent signs and the separators between them. The function recognizes three types of Sumerian sign representations: lowercase transliterations, uppercase sign names, and Unicode cuneiform characters.

Usage

split_sumerian(x)

Arguments

Details

The function identifies Sumerian signs based on three patterns:

1. Lowercase transliterations (type 1): Sequences of lowercase letters (a-z) including special characters (ĝ, š, ...) and accented vowels (á, é, í, ú, à, è, ì, ù), optionally followed by a numeric index.

2. Uppercase sign names (type 2): Sequences starting with an uppercase letter, optionally followed by additional uppercase letters, digits, or the characters +, /, and ×.

3. Cuneiform characters (type 3): Unicode characters in the Cuneiform block (U+12000 to U+12500).

The function returns the signs and separators in a format that allows exact reconstruction of the original string using paste0(c("", signs), separators, collapse = "").

Value

A list with three components:

Examples

# Example 1

set.seed(4)

x <- "en-tarah-an-na-ke4"

result <- split_sumerian(x)

result

# Example 2

x <- "en-DARA3.AN.na-ke4"

result <- split_sumerian(x)

result

# Reconstruct the original string
paste0(c("", result$signs), result$separators, collapse = "")

Compute Row Index of a Substring in a Substring Data Frame

Description

Computes the row index of a substring in the data frame created by init_substr_info, given its starting position, its length in tokens, and the total number of tokens.

This is an internal helper function.

Usage

substr_position(start, n_tokens, N)

Arguments

Details

The data frame returned by init_substr_info is ordered by n_tokens descending and start ascending. This function computes the corresponding row index using the formula

\mathrm{row} = \frac{(N - k)(N - k + 1)}{2} + s

where k = n_tokens and s = start.

The function is vectorized: if start and n_tokens are vectors of the same length, a vector of row indices is returned.

Value

A numeric vector of row indices (1-based).

See Also

init_substr_info for creating the substring data frame

Examples

# Create a character vector with tokens
x <- "<d-nu-dim2-mud> ki a. jal2 (e2{kur}) ra. gaba jal2. an ki a"
token <- split_sumerian(as.cuneiform(x))$signs
token

N <- length(token)

# Create a data frame with all substrings
df <- sumer:::init_substr_info(token)

# The full string (start=1, n_tokens=N) is in row 1
pos <- sumer:::substr_position(1, N, N)
pos
df$expr[pos]


# The last single token (start=N, n_tokens=1) is in the last row
pos <- sumer:::substr_position(N, 1, N)
pos
df$expr[pos]

# Vectorized call
start <- c(1, 2, 1)
n_token <- c(2, 2, 1)
pos <- sumer:::substr_position(start, n_token, N)
pos
df$expr[pos]

Interactive Translation Tool for Sumerian Text

Description

Opens an interactive Shiny gadget for translating a single line of Sumerian cuneiform text. The page displays four sections on a single scrollable page: n-gram patterns, context with neighbouring lines, grammar probabilities, and an interactive skeleton with dictionary lookup. When the user clicks “Done”, the function returns a skeleton object with the translations.

Usage

translate(x, text = NULL, dic = NULL, mapping = NULL, fill = NULL,
          min_freq = c(6, 4, 2), sentence_prob = 1.0)

Arguments

Details

The gadget opens as a dialog window in RStudio (via shiny::dialogViewer()) and displays four sections on a single scrollable page. The first three sections (N-grams, Context, Grammar) can be collapsed individually. A sticky navigation menu at the top allows jumping to each section.

N-gram Patterns

Displays a merged table of n-gram combinations that appear in the current line: n-grams of length 2 or more from the full text (controlled by min_freq), combined with shared n-grams found in neighbouring lines. A “Theme” column marks n-grams shared with the context. Frequencies refer to the full text.

Context

Shows neighbouring lines (up to 2 before and after) with frequent n-grams marked. Only available when text is provided and the line index is known.

Grammar Probabilities

Displays a bar chart of grammar probabilities for each sign in the line, computed via grammar_probs with the given sentence_prob.

Translation

The main interactive section with dictionary selection checkboxes, a bracket input field for editing the skeleton structure, an interactive skeleton display with type and translation fields, and a dictionary lookup panel. Clicking a dictionary row adopts its type and translation into the selected skeleton entry.

When the line contains multiple sentences (separated by dots in the transliteration), skeleton entries belonging to different sentences are displayed with alternating background colours.

The bracket input field allows the user to add or modify brackets (), <>, {} to control the grouping structure of the skeleton. Pressing “Update Skeleton” rebuilds the skeleton display while keeping all translations made so far in memory.

Value

A skeleton object (character vector of class c("skeleton", "character")), generated by calling skeleton with the final bracket string and updated fill data frame. Returns invisible(NULL) if the user closes the window without clicking “Done”.

Note

The gadget opens as a dialog window in RStudio. Outside of RStudio, it falls back to the system browser.

See Also

skeleton for creating translation templates, guess_substr_info for pre-computing substring translations, look_up for interactive dictionary lookup, ngram_frequencies for n-gram analysis, grammar_probs for grammar probability computation, prior_probs for prior probability computation, mark_ngrams for marking n-grams in text

Examples

## Not run: 

# Basic usage with a transliterated string
result <- translate("lugal kur-ra-ke4")

# Usage together with a complete text
x <- "<d-nu-dim2-mud> ki a. jal2 (e2-kur) ra. gaba jal2. an ki a"

dict_file <- system.file("extdata", "sumer-dictionary.txt", package = "sumer")
text_file <- system.file("extdata", "enki_and_the_world_order.txt", package = "sumer")

result <- translate(x,
                text = text_file,
                dic = dict_file,
                min_freq = c(6, 4, 2),
                sentence_prob = 0.25)
print(result)

# Try also with: x <- 9

## End(Not run)