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#' Updater function for elasticizer: Conduct actor searches
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#'
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#' Updater function for elasticizer: Conduct actor searches
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#' @param out Does not need to be defined explicitly! (is already parsed in the elasticizer function)
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#' @param localhost Defaults to false. When true, connect to a local Elasticsearch instance on the default port (9200)
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#' @param ids List of actor ids
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#' @param prefix Regex containing prefixes that should be excluded from hits
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#' @param postfix Regex containing postfixes that should be excluded from hits
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#' @param identifier String used to mark highlights. Should be a lowercase string
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#' @param ver Short string (preferably a single word/sequence) indicating the version of the updated document (i.e. for a udpipe update this string might be 'udV2')
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#' @param es_super Password for write access to ElasticSearch
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#' @param cores Number of cores to use for parallel processing, defaults to cores (all cores available)
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#' @return As this is a nested function used within elasticizer, there is no return output
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#' @export
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#' @examples
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#' actorizer(out, localhost = F, ids, prefix, postfix, identifier, es_super)
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actorizer_old <- function(out, localhost = F, ids, prefix, postfix, pre_tags, post_tags, es_super, ver, cores = 1) {
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plan(multiprocess, workers = cores)
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### Function to filter out false positives using regex
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exceptionizer <- function(id, ud, doc, markers, pre_tags_regex, post_tags_regex,pre_tags,post_tags, prefix, postfix) {
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min <- min(ud$start[ud$sentence_id == id]) # Get start position of sentence
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max <- max(ud$end[ud$sentence_id == id]) # Get end position of sentence
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split <- markers[markers %in% seq(min, max, 1)] # Get markers in sentence
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min <- min+((nchar(pre_tags)+nchar(post_tags))*((match(split,markers))-1))
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max <- max+((nchar(pre_tags)+nchar(post_tags))*match(split,markers)) # Set end position to include markers (e.g if there are two markers of three characters in the sentence, the end position needs to be shifted by +6)
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sentence <- paste0(' ',str_sub(doc$merged, min, max),' ') # Extract sentence from text, adding whitespaces before and after for double negation (i.e. Con only when preceded by "("))
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# Check if none of the regexes match, if so, return sentence id, otherwise (if one of the regexes match) return nothing
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if (!str_detect(sentence, paste0(post_tags_regex,'(',postfix,')')) && !str_detect(sentence, paste0('(',prefix,')',pre_tags_regex))) {
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return(id)
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} else {
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return(NULL)
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}
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}
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ranger <- function(x, ud) {
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return(which((ud$start <= x) & (ud$end >= x)))
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}
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sentencizer <- function(row, out, ids, prefix, postfix, pre_tags, post_tags, pre_tags_regex, post_tags_regex) {
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doc <- out[row,]
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if (nchar(doc$merged) > 990000) {
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return(
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data.frame(
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err = T,
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errorMessage = "Merged document exceeded 990000 characters, highlighting possibly incorrect"
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)
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)
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}
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# Extracting ud output from document
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ud <- doc$`_source.ud`[[1]] %>%
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select(-one_of('exists')) %>% # Removing ud.exists variable
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unnest() %>%
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mutate(doc_id = doc$`_id`)
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markers <- doc$markers[[1]][,'start'] # Extract list of markers
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# Convert markers to udpipe rows (in some cases the start position doesn't align with the udpipe token start position (e.g. when anti-|||EU is treated as a single word))
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rows <- unlist(lapply(markers, ranger, ud = ud))
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# Setting up an actor variable
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ud$actor <- F
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ud$actor[rows] <- T
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sentence_count <- max(ud$sentence_id) # Number of sentences in article
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actor_sentences <- unique(ud$sentence_id[ud$actor]) # Sentence ids of sentences mentioning actor
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# Conducting regex filtering on matches only when there is a prefix and/or postfix to apply
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if (!is.na(prefix) || !is.na(postfix)) {
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### If no pre or postfixes, match *not nothing* i.e. anything
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if (is.na(prefix)) {
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prefix = '$^'
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}
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if (is.na(postfix)) {
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postfix = '$^'
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}
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sentence_ids <- unlist(lapply(actor_sentences,
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exceptionizer,
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ud = ud,
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doc = doc,
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markers = markers,
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pre_tags_regex = pre_tags_regex,
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pre_tags = pre_tags,
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post_tags_regex = post_tags_regex,
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post_tags = post_tags,
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prefix = prefix,
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postfix = postfix))
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} else {
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sentence_ids <- actor_sentences
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}
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if (length(sentence_ids > 0)) {
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# Generating nested sentence start and end positions for actor sentences
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ud <- ud %>%
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filter(sentence_id %in% sentence_ids)
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actor_start <- ud$start[ud$actor == T] # Udpipe token start positions for actor
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actor_end <- ud$end[ud$actor == T] # Udpipe token end positions for actor
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ud <- ud %>%
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group_by(sentence_id) %>%
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summarise (
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sentence_start = as.integer(min(start)),
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sentence_end = as.integer(max(end)),
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doc_id = first(doc_id)
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) %>%
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group_by(doc_id) %>%
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summarise(
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sentence_id = list(as.integer(sentence_id)),
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sentence_start = list(sentence_start),
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sentence_end = list(sentence_end)
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)
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return(
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data.frame(ud, # Sentence id, start and end position for actor sentences
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actor_start = I(list(actor_start)), # List of actor ud token start positions
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actor_end = I(list(actor_end)), # List of actor ud token end positions
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occ = length(unique(sentence_ids)), # Number of sentences in which actor occurs
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prom = length(unique(sentence_ids))/sentence_count, # Relative prominence of actor in article (number of occurences/total # sentences)
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rel_first = 1-(min(sentence_ids)/sentence_count), # Relative position of first occurence at sentence level
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first = min(sentence_ids), # First sentence in which actor is mentioned
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ids = I(list(ids)) # List of actor ids
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)
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)
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} else {
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return(NULL)
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}
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}
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out <- mamlr:::out_parser(out, field = 'highlight', clean = F)
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offsetter <- function(x, pre_tags, post_tags) {
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return(x-((row(x)-1)*(nchar(pre_tags)+nchar(post_tags))))
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}
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prefix[prefix==''] <- NA
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postfix[postfix==''] <- NA
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pre_tags_regex <- gsub("([.|()\\^{}+$*?]|\\[|\\])", "\\\\\\1", pre_tags)
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post_tags_regex <- gsub("([.|()\\^{}+$*?]|\\[|\\])", "\\\\\\1", post_tags)
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out$markers <- future_lapply(str_locate_all(out$merged,coll(pre_tags)), offsetter, pre_tags = pre_tags, post_tags = post_tags)
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# ids <- fromJSON(ids)
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updates <- bind_rows(future_lapply(seq(1,length(out[[1]]),1), sentencizer,
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out = out,
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ids = ids,
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postfix = postfix,
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prefix=prefix,
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pre_tags_regex = pre_tags_regex,
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pre_tags = pre_tags,
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post_tags_regex = post_tags_regex,
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post_tags = post_tags))
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if (nrow(updates) == 0) {
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print("Nothing to update for this batch")
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return(NULL)
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} else {
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bulk <- apply(updates, 1, bulk_writer, varname ='actorsDetail', type = 'add', ver = ver)
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bulk <- c(bulk,apply(updates[c(1,11)], 1, bulk_writer, varname='actors', type = 'add', ver = ver))
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return(elastic_update(bulk, es_super = es_super, localhost = localhost))
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}
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}
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