This post outlines a simple framework for identifying and extracting keyphrases from component texts of a corpus. We first consider some functional characteristics of descriptive keyphrases, as well as some more formal (ie, regex-based) definitions.
We then demonstrate the use of
corpuslingr for identifying potential keyphrases in an annotated corpus, and present an unsupervised (and well-established) methodology (tf-idf weights) for extracting descriptive keyphrases for each text.
The Slate Magazine corpus from the
corpusdatr package is used here for demo purposes.
library(corpusdatr) #devtools::install_github("jaytimm/corpusdatr") library(corpuslingr) #devtools::install_github("jaytimm/corpuslingr") library(tidyverse) library(DT)
Defining potential keyphrases
Ideally, keyphrases are semantically rich noun phrases that shed light on the content of a particular text. For illustrative purposes, three noun phrases of varying degrees of complexity and informativeness are presented below:
The first is comprised solely of a plural noun form; the second is comprised of a noun form modified by an adjective; the third is comprised of a noun phrase modified by a prepositional phrase (which contains another noun phrase). By virtue of specifying both the type and “where” of flower, the latter would seem to have the most descriptive utility to someone perusing content (or some algorithm classifying texts) via keyphrases.
From a regex perspective, then, we want to create a search template that is as greedy as possible when it comes to noun phrase constituency; in other words, while we will settle for unmodified noun forms as keyphrases, we prefer highly modified ones. And we are not interested in pronominal forms.
So, we define a noun phrase as “zero or more adjectives followed by one or more nouns” and define potential keyphrases as follows
Noun phrase+ ( preposition +
where the prepositional phrase is optional. This schema maps generically to the regex below (per Penn Treebank POS codes):
nounPhrase < - "(JJ[A-Z]* )*(NN[A-Z]* )+" prepPhrase <- paste0("((IN )",nounPhrase,")?") keyPhrase <- paste0(nounPhrase,prepPhrase) keyPhrase
##  "(JJ[A-Z]* )*(NN[A-Z]* )+((IN )(JJ[A-Z]* )*(NN[A-Z]* )+)?"
Using the simplifying CQL made available via
corpuslingr, the above regex is written as:
keyPhrase < - "(ADJ )*(NOUNX )+((PREP )(ADJ )*(NOUNX )+)?"
Corpus search for potential keyphrases
Per this definition, the next step is to search the Slate magazine corpus for potential keyphrases. So, we first set the corpus for search (within the
corpuslingr framework) using the
slate < - corpusdatr::cdr_slate_ann %>% corpuslingr::clr_set_corpus()
Then we use the
corpuslingr::clr_search_gramx function to extract all potential keyphrases from each text in the corpus:
kps < - corpuslingr::clr_search_gramx(search=keyPhrase, corp=slate)
Example output returned by
## doc_id token tag lemma ## 1: 1 rulers NNS ruler ## 2: 1 world NN world ## 3: 1 populous countries JJ NNS populous country ## 4: 1 hold on power NN IN NN hold on power ## 5: 1 Indonesia NNP Indonesia ## 6: 1 Suharto NNP Suharto
The plot below illustrates the top fifteen instantiations of our keyphrase regex search in the Slate Magazine corpus. While the top two instantiations are unmodified noun phrases, multi-word noun phrases constitute a sizable portion of potential keyphrases as well.
kps%>% corpuslingr::clr_get_freq(agg_var = 'tag')%>% top_n(15,txtf)%>% ggplot(aes(x=reorder(tag, txtf), y=txtf)) + geom_col(width=.65, fill="cyan4") + coord_flip()+ theme_bw() + labs(title = "Top 15 keyphrases by pattern type")
Selecting descriptive keyphrases with the tf-idf statisitic
The term frequency – inverse document frequency (tf-idf) statistic is a super simple, unsupervised approach to keyphrase extraction. As a metric it is meant to capture (or weigh) how frequent a given phrase is in a text (ie, text frequency) relative to how dispersed the phrase is across documents comprising the corpus (ie, document frequency).
Phrases occurring more frequently in a given text than we would expect based on their document frequency receive higher weights; theoretically, such phrases shed light on the content of a given text (relative to the content of the corpus as a whole).
The tf-idf weight for a given keyphrase in a given document, then, is calculated as the product of token frequency, tf, and inverse document frequency, idf, where the latter is logarithmically transformed.
Here we compute text frequency and document frequency for each keyphrase, and join metadata from
cdr_slate_meta, which includes texts titles and descriptives. Frequencies are aggregated by keyphrase lemma constituents.
kpsAgg < - kps %>% group_by(lemma)%>% mutate(docf=length(unique(doc_id)))%>% group_by(doc_id,lemma,docf)%>% summarize(txtf=n())%>% left_join(corpusdatr::cdr_slate_meta)%>% mutate(docsInCorpus=length(cdr_slate_ann))%>% select(doc_id,title,lemma,txtf,textLength,docf,docsInCorpus)
Based on these two (relative) frequencies, we compute td-idf values for each keyphrase in each document, collapse the top five phrases into a single cell, and wrap the table up with some
kpsAgg%>% mutate(td_idf = (txtf/textLength)*log(docsInCorpus/(docf+1)))%>% mutate(td_idf=jitter(td_idf))%>% group_by(doc_id,title)%>% top_n(5,td_idf)%>% arrange(doc_id,desc(td_idf))%>% group_by(doc_id,title)%>% summarise(key_phrases = paste(lemma, collapse=" | "))%>% arrange(as.numeric(doc_id))%>% DT::datatable(class = 'cell-border stripe', rownames = FALSE, width="450", escape=FALSE)