Chapter 4 Stemming
When we deal with text, often documents contain different versions of one base word, often called a stem. “The Fir-Tree”, for example, contains more than one version (i.e., inflected form) of the word
library(hcandersenr) library(tidyverse) library(tidytext) fir_tree <- hca_fairytales() %>% filter( book == "The fir tree", language == "English" ) tidy_fir_tree <- fir_tree %>% unnest_tokens(word, text) %>% anti_join(get_stopwords()) tidy_fir_tree %>% count(word, sort = TRUE) %>% filter(str_detect(word, "^tree"))
## # A tibble: 3 x 2 ## word n ## <chr> <int> ## 1 tree 76 ## 2 trees 12 ## 3 tree's 1
Trees, we see once again, are important in this story; the singular form appears 76 times and the plural form appears twelve times. (We’ll come back to how we might handle the apostrophe in
"tree's" later in this chapter.)
What if we aren’t interested in the difference between
"tree" and we want to treat both together? That idea is at the heart of stemming, the process of identifying the base word (or stem) for a dataset of words. Stemming is concerned with the linguistics subfield of morphology, how words are formed. In this example,
"trees" would lose its letter
"tree" stays the same. If we counted word frequencies again after stemming, we would find that there are 88 occurrences of the stem
"tree" (89, if we also find the stem for
4.1 How to stem text in R
There have been many algorithms built for stemming words over the past half century or so; we’ll focus on two approaches. The first is the stemming algorithm of Porter (1980), probably the most widely used stemmer for English. Porter himself released the algorithm implemented in the framework Snowball with an open-source license; you can use it from R via the SnowballC package. (It has been extended to languages other than English as well.)
## # A tibble: 570 x 2 ## stem n ## <chr> <int> ## 1 tree 88 ## 2 fir 34 ## 3 littl 23 ## 4 said 22 ## 5 stori 16 ## 6 thought 16 ## 7 branch 15 ## 8 on 15 ## 9 came 14 ## 10 know 14 ## # … with 560 more rows
Take a look at those stems. Notice that we do now have 88 incidences of
"tree". Also notice that some words don’t look like they are spelled as real words; this is normal and expected with this stemming algorithm. The Porter algorithm identifies the stem of both
"stori", not a regular English word but instead a special stem object.
If you want to tokenize and stem your text data, you can try out the function
tokenize_word_stems() from the tokenizers package, which implements Porter stemming just like what we demonstrated here. For more on tokenization, see Chapter 2.
Does Porter stemming only work for English? Far from it! We can use the
language argument to implement Porter stemming in multiple languages. First we can tokenize the text and
nest() into list-columns.
stopword_df <- tribble( ~language, ~two_letter, "danish", "da", "english", "en", "french", "fr", "german", "de", "spanish", "es" ) tidy_by_lang <- hca_fairytales() %>% filter(book == "The fir tree") %>% select(text, language) %>% mutate(language = str_to_lower(language)) %>% unnest_tokens(word, text) %>% nest(data = word)
Then we can remove stop words (using
get_stopwords(language = "da") and similar for each language) and stem with the language-specific Porter algorithm. What are the top 20 stems for “The Fir-Tree” in each of these five languages, after removing the Snowball stop words for that language?
tidy_by_lang %>% inner_join(stopword_df) %>% mutate(data = map2( data, two_letter, ~ anti_join(.x, get_stopwords(language = .y)) )) %>% unnest(data) %>% mutate(stem = wordStem(word, language = language)) %>% group_by(language) %>% count(stem) %>% top_n(20, n) %>% ungroup() %>% ggplot(aes(n, fct_reorder(stem, n), fill = language)) + geom_col(show.legend = FALSE) + facet_wrap(~language, scales = "free_y", ncol = 2) + labs(x = "Frequency", y = NULL)
Figure 4.1 demonstrates some of the challenges in working with languages other English; the stop word lists may not be even from language to language, and tokenization strategies that work for a language like English may struggle for a language like French with more stop word contractions. Given that, we see here words about little fir trees at the top for all languages, in their stemmed forms.
The Porter stemmer is an algorithm that starts with a word and ends up with a single stem, but that’s not the only kind of stemmer out there. Another class of stemmer are dictionary-based stemmers. One such stemmer is the stemming algorithm of the Hunspell library. The “Hun” in Hunspell stands for Hungarian; this set of NLP algorithms was originally written to handle Hungarian but has since been extended to handle many languages with compound words and complicated morphology. The Hunspell library is used mostly as a spell checker, but as part of identifying correct spellings, this library identifies word stems as well. You can use the Hunspell library from R via the hunspell package.
## # A tibble: 595 x 2 ## stem n ## <chr> <int> ## 1 tree 89 ## 2 fir 34 ## 3 little 23 ## 4 said 22 ## 5 story 16 ## 6 branch 15 ## 7 one 15 ## 8 came 14 ## 9 know 14 ## 10 now 14 ## # … with 585 more rows
Notice that the code here is a little different (we had to use
unnest()) and that the results are a little different. We have only real English words, and we have more total rows in the result. What happened?
## [] ##  "contented" "content"
We have two stems! This stemmer works differently; it uses both morphological analysis of a word and existing dictionaries to find possible stems. It’s possible to end up with more than one, and it’s possible for a stem to be a word that is not related by meaning to the original word. For example, one of the stems of “number” is “numb” with this library. The Hunspell library was built to be a spell checker, so depending on your analytical purposes, it may not be an appropriate choice.
4.2 Should you use stemming at all?
You will often see stemming as part of NLP pipelines, sometimes without much comment about when it is helpful or not. We encourage you to think of stemming as a preprocessing step in text modeling, one that must be thought through and chosen (or not) with good judgment.
Why does stemming often help, if you are training a machine learning model for text? Stemming reduces the feature space of text data. Let’s see this in action, with a dataset of United States Supreme Court opinions available in the scotus package. How many words are there, after removing a standard dataset of stopwords?
## # A tibble: 167,879 x 2 ## word n ## <chr> <int> ## 1 court 286448 ## 2 v 204176 ## 3 state 148320 ## 4 states 128160 ## 5 case 121439 ## 6 act 111033 ## 7 s.ct 108168 ## 8 u.s 106413 ## 9 upon 105069 ## 10 united 103267 ## # … with 167,869 more rows
There are 167,879 distinct words in this dataset we have created (after removing stopwords) but notice that even in the most common words we see a pair like
"states". A common data structure for modeling, and a helpful mental model for thinking about the sparsity of text data, is a matrix. Let’s
cast() this tidy data to a sparse matrix (technically, a document-feature matrix object from the quanteda package).
## Document-feature matrix of: 9,642 documents, 167,879 features (99.5% sparse).
Look at the sparsity of this matrix. It’s high! Think of this sparsity as the sparsity of data that we will want to use to build a supervised machine learning model.
What if instead we use stemming as a preprocessing step here?
## Document-feature matrix of: 9,642 documents, 135,570 features (99.5% sparse).
We reduced the number of word features by many thousands, although the sparsity did not change much. Why is it possibly helpful to reduce the number of features? Common sense says that reducing the number of word features in our dataset so dramatically will improve the performance of any machine learning model we train with it, assuming that we haven’t lost any important information by stemming.
There is a growing body of academic research demonstrating that stemming can be counterproductive for text modeling. For example, Schofield and Mimno (2016) and related work explore how choices around stemming and other preprocessing steps don’t help and can actually hurt performance when training topic models for text. From Schofield and Mimno (2016) specifically,
Despite their frequent use in topic modeling, we find that stemmers produce no meaningful improvement in likelihood and coherence and in fact can degrade topic stability.
Topic modeling is an example of unsupervised machine learning for text and is not the same as the predictive modeling approaches we’ll be focusing on in this book, but the lesson remains that stemming may or may not be beneficial for any specific context. As we work through the rest of this chapter and learn more about stemming, consider what information we lose when we stem text in exchange for reducing the number of word features. Stemming can be helpful in some contexts, but typical stemming algorithms are somewhat aggressive and have been built to favor sensitivity (or recall, or the true positive rate) at the expense of specificity (or precision, or the true negative rate).
Most common stemming algorithms you are likely to encounter will successfully reduce words to stems (i.e., not leave extraneous word endings on the words) but at the expense of collapsing some words with dramatic differences in meaning, semantics, use, etc. to the same stems. Examples of the latter are numerous, but some include:
- meaning and mean
- likely, like, liking
- university and universe
In a supervised machine learning context, this affects a model’s positive predictive value (precision), or ability to not incorrectly label true negatives as positive. In Chapter 7, we will train models to predict whether a complaint to the United States Consumer Financial Protection Bureau was about a mortgage or not. Stemming can increase a model’s ability to find the positive examples, i.e., the complaints about mortgages. However, if the complaint text is over-stemmed, the resulting model loses its ability to label the negative examples, the complaints not about mortgages, correctly.
4.3 Understand a stemming algorithm
If stemming is going to be in our NLP toolbox, it’s worth sitting down with one approach in detail to understand how it works under the hood. The Porter stemming algorithm is so approachable that we can walk through its outline in less than a page or so. It involves five steps, and the idea of a word measure.
Think of any word as made up alternating groups of vowels \(V\) and consonants \(C\). One or more vowels together are one instance of \(V\), and one or more consonants togther are one instance of \(C\). We can write any word as
where \(m\) is called the “measure” of the word. The first \(C\) and the last \(V\) in brackets are optional. In this framework, we could write out the word
with \(C\) being “tr” and \(V\) being “ee”; it’s an
m = 0 word. We would write out the word
and it is an
m = 3 word.
- The first step of the Porter stemmer is (perhaps this seems like cheating) actually made of three substeps working with plural and past participle word endings. In the first substep (1a), “sses” is replaced with “ss”, “ies” is replaced with “i”, and final single “s” letters are removed. The second substep (1b) depends on the measure of the word
mbut works with endings like “eed”, “ed”, “ing”, adding “e” back on to make endings like “ate”, “ble”, and “ize” when appropriate. The third substep (1c) replaces “y” with “i” for words of a certain
- The second step of the Porter stemmer takes the output of the first step and regularizes a set of 20 endings. In this step, “ization” goes to “ize”, “alism” goes to “al”, “aliti” goes to “al” (notice that the ending “i” there came from the first step), and so on for the other 17 endings.
- The third step again processes the output, using a list of seven endings. Here, “ical” and “iciti” both go to “ic”, “ful” and “ness” are both removed, and so forth for the three other endings in this step.
- The fourth step involves a longer list of endings to deal with again (19), and they are all removed. Endings like “ent”, “ism”, “ment”, and more are removed in this step.
- The fifth and final step has two substeps, both which depend on the measure
mof the word. In this step, depending on
m, final “e” letters are sometimes removed and final double letters are sometimes removed.
How would this work for a few example words? The word “supervised” loses its “ed” in step 1b and is not touched by the rest of the algorithm, ending at “supervis”. The word “relational” changes “ational” to “ate” in step 2 and loses its final “e” in step 5, ending at “relat”. Notice that neither of these results are regular English words, but instead special stem objects. This is expected.
This algorithm was first published in Porter (1980) and is still broadly used; read Willett (2006) for background on how and why it has become a stemming standard. We can reach even further back and examine what is considered the first ever published stemming algorithm in Lovins (1968). The domain Lovins worked in was engineering, so her approach was particularly suited to technical terms. This algorithm uses much larger lists of word endings, conditions, and rules than the Porter algorithm and, although considered old-fashioned, is actually faster!
Check out the steps of a Snowball stemming algorithm for German.
4.4 Handling punctuation when stemming
Punctuation contains information that can be used in text analysis. Punctuation is typically less information-dense than the words themselves and thus it is often removed early in a text mining analysis project, but it’s worth thinking through the impact of punctuation specifically on stemming. Think about words like
We’ve already seen how punctuation and stemming can interact with our small example of “The Fir-Tree”; none of the stemming strategies we’ve discussed so far have recognized
"tree's" as belonging to the same stem as
## # A tibble: 3 x 2 ## word n ## <chr> <int> ## 1 tree 76 ## 2 trees 12 ## 3 tree's 1
It is possible to split tokens not only on white space but also on punctuation, using a regular expression (see Appendix 10).
## # A tibble: 572 x 2 ## stem n ## <chr> <int> ## 1 tree 89 ## 2 fir 34 ## 3 littl 23 ## 4 said 22 ## 5 stori 16 ## 6 thought 16 ## 7 branch 15 ## 8 on 15 ## 9 came 14 ## 10 know 14 ## # … with 562 more rows
Now we are able to put all these related words together, having identified them with the same stem.
## # A tibble: 1 x 2 ## stem n ## <chr> <int> ## 1 tree 89
Handling punctuation in this way further reduces sparsity in word features. Whether this kind of tokenization and stemming strategy is a good choice in any particular data analysis situation depends on the particulars of the text characteristics.
4.5 Compare some stemming options
Let’s compare a few simple stemming algorithms and see what results we end with. Let’s look at “The Fir-Tree”, specifically the tidied dataset from which we have removed stop words. Let’s compare three very straightforward stemming approaches.
- Only remove final instances of the letter “s”. This probably strikes you as not a great idea after our discussion in this chapter, but it is something that people try in real life, so let’s see what the impact is.
- Handle plural endings with slightly more complex rules in the “S” stemmer. The S-removal stemmer or “S” stemmer of Harman (1991) is a simple algorithm with only three rules.3
- Implement actual Porter stemming. We can now compare to the most commonly used stemming algorithm in English.
stemming <- tidy_fir_tree %>% select(-book, -language) %>% mutate( `Remove S` = str_remove(word, "s$"), `Plural endings` = case_when( str_detect(word, "[^e|aies$]ies$") ~ str_replace(word, "ies$", "y"), str_detect(word, "[^e|a|oes$]es$") ~ str_replace(word, "es$", "e"), str_detect(word, "[^ss$|us$]s$") ~ str_remove(word, "s$"), TRUE ~ word ), `Porter stemming` = wordStem(word) ) %>% rename(`Original word` = word)
Figure 4.2 shows the results of these stemming strategies. All successfully handled the transition from
"tree" in the same way, but we have different results for
"stori", different handling of
"branches", and more. There are subtle differences in the output of even these straightforward stemming approaches that can effect the transformation of text features for modeling.
stemming %>% gather(Type, Result, `Remove S`:`Porter stemming`) %>% mutate(Type = fct_inorder(Type)) %>% count(Type, Result) %>% group_by(Type) %>% top_n(20, n) %>% ungroup() %>% ggplot(aes(fct_reorder(Result, n), n, fill = Type )) + geom_col(show.legend = FALSE) + facet_wrap(~Type, scales = "free_y") + coord_flip() + labs(x = NULL, y = "Frequency")
Porter stemming is the most different from the other two approaches. In the top twenty words here, we don’t see a difference between removing only the letter “s” and taking the slightly more sophisticated “S” stemmer approach to plural endings. In what situations do we see a difference?
## # A tibble: 13 x 4 ## `Original word` `Remove S` `Plural endings` `Porter stemming` ## <chr> <chr> <chr> <chr> ## 1 raspberries raspberrie raspberry raspberri ## 2 strawberries strawberrie strawberry strawberri ## 3 less les less less ## 4 us u us u ## 5 brightness brightnes brightness bright ## 6 conscious consciou conscious consciou ## 7 faintness faintnes faintness faint ## 8 happiness happines happiness happi ## 9 ladies ladie lady ladi ## 10 babies babie baby babi ## 11 anxious anxiou anxious anxiou ## 12 princess princes princess princess ## 13 stories storie story stori
We also see situations where the same sets of original words are bucketed differently (not just with different stem labels) under different stemming strategies. In the following very small example, two of the strategies bucket these words into two stems while one strategy buckets them into one stem.
## # A tibble: 9 x 3 ## `Original word` Type Result ## <chr> <chr> <chr> ## 1 come Remove S come ## 2 comes Remove S come ## 3 coming Remove S coming ## 4 come Plural endings come ## 5 comes Plural endings come ## 6 coming Plural endings coming ## 7 come Porter stemming come ## 8 comes Porter stemming come ## 9 coming Porter stemming come
These different characteristics can either be positive or negative, depending on the nature of the text being modeled and the analytical question being pursued.
Language use is connected to culture and identity. How might the results of stemming strategies be different for text created with the same language (like English) but in different social or cultural contexts, or by people with different identities? With what kind of text do you think stemming algorithms behave most consistently, or most as expected? What impact might that have on text modeling?
4.6 Lemmatization and stemming
When people use the word “stemming” in natural language processing, they typically mean a system like the one we’ve been describing in this chapter, with rules, conditions, heuristics, and lists of word endings. Think of stemming as typically implemented in NLP as rule-based, operating on the word by itself. There is another option for normalizing words to a root that takes a different approach. Instead of using rules to cut words down to their stems, lemmatization uses knowledge about a language’s structure to reduce words down to their lemmas, the canonical or dictionary forms of words. Think of lemmatization as typically implemented in NLP as linguistics-based, operating on the word in its context.
Lemmatization requires more information than the rule-based stemmers we’ve discussed so far. We need to know what part of speech a word is to correctly identify its lemma,4 and we also need more information about what words mean in their contexts. Often lemmatizers use a rich lexical database like WordNet as a way to look up word meanings for a given part-of-speech use (Miller 1995). Notice that lemmatization involves more linguistic knowledge of a language than stemming.
How does lemmatization work in languages other than English? Lookup dictionaries connecting words, lemmas, and parts of speech for languages other than English have been developed as well.
A modern, efficient implementation for lemmatization is available in the excellent spaCy library (Honnibal and Montani 2017), which is written in Python. NLP practitioners who work with R can use this library via the spacyr package (Benoit and Matsuo 2019), the cleanNLP package (Arnold 2017), or as an “engine” in the textrecipes package (Hvitfeldt 2020b). Section 6.7 demonstrates how to use textrecipes with spaCy as an engine and include lemmas as features for modeling. You might also consider using spaCy directly in R Markdown via its Python engine.
Let’s briefly walk through how to use spacyr.
library(spacyr) spacy_initialize(entity = FALSE) fir_tree %>% mutate(doc_id = paste0("doc", row_number())) %>% select(doc_id, everything()) %>% spacy_parse() %>% anti_join(get_stopwords(), by = c("lemma" = "word")) %>% count(lemma, sort = TRUE) %>% top_n(20, n) %>% ggplot(aes(n, fct_reorder(lemma, n))) + geom_col() + labs(x = "Frequency", y = NULL)
Figure 4.3 demonstrates how different lemmatization is from stemming, especially is we compare to Figure 4.2. Punctuation characters are treated as tokens (these punctuation tokens can have predictive power for some modeling questions!) and all pronouns are lemmatized to
-PRON-. We see our familiar friends “tree” and “fir”, but notice that we see the normalized version “say” instead of “said”, “come” instead of “came”, and similar. This transformation to the canonical or dictionary form of words is the goal of lemmatization.
Why did we need to initialize the spaCy library? You may not need to, but spaCy is a full-featured NLP pipeline that not only tokenizes and identifies lemmas but also performs entity recognition. We will not use entity recognition in modeling or analysis in this book and it takes a lot of computational power. Initializing with
entity = FALSE will allow lemmatization to run much faster.
Implementing lemmatization is slower and more complex than stemming. Just like with stemming, lemmatization often improves the true positive rate (or recall) but at the expense of the true negative rate (or precision) compared to not using lemmatization, but typically less so than stemming.
4.7 Stemming and stop words
Our deep dive into stemming came after our chapters on tokenization (Chapter 2) and stop words (Chapter 3) because this is typically when you will want to implement stemming, if appropriate to your analytical question. Stop word lists are usually unstemmed, so you need to remove stop words before stemming text data. For example, the Porter stemming algorithm transforms words like
"themselv", so stemming first would leave you without the ability to match up to the commonly used stop word lexicons.
A handy trick is to use the following function on your stop word list to return the words that don’t have a stemmed version in the list. If the function returns a length 0 vector then you can stem and remove stop words in any order.
##  "ourselves" "yourselves" "his" "they" "themselves" ##  "this" "are" "was" "has" "does" ##  "you're" "he's" "she's" "it's" "we're" ##  "they're" "i've" "you've" "we've" "they've" ##  "let's" "that's" "who's" "what's" "here's" ##  "there's" "when's" "where's" "why's" "how's" ##  "because" "during" "before" "above" "once" ##  "any" "only" "very"
Here we see that many of the words that are lost are the contractions.
In this chapter, we explored stemming, the practice of identifying and extracting the base or stem for a word using rules and heuristics. Stemming reduces the sparsity of text data which can be helpful when training models, but at the cost of throwing information away. Lemmatization is another way to normalize words to a root, based on language structure and how words are used in their context.
4.8.1 In this chapter, you learned:
- about the most broadly used stemming algorithms
- how to implement stemming
- that stemming changes the sparsity or feature space of text data
- the differences between stemming and lemmatization
Arnold, Taylor. 2017. “A Tidy Data Model for Natural Language Processing Using cleanNLP.” The R Journal 9 (2): 1–20. https://journal.r-project.org/archive/2017/RJ-2017-035/index.html.
Benoit, Kenneth, and Akitaka Matsuo. 2019. Spacyr: Wrapper to the ’spaCy’ ’Nlp’ Library. https://CRAN.R-project.org/package=spacyr.
Harman, Donna. 1991. “How Effective Is Suffixing?” Journal of the American Society for Information Science 42 (1): 7–15.
Honnibal, Matthew, and Ines Montani. 2017. “spaCy 2: Natural Language Understanding with Bloom Embeddings, Convolutional Neural Networks and Incremental Parsing.”
Hvitfeldt, Emil. 2020b. Textrecipes: Extra ’Recipes’ for Text Processing. https://CRAN.R-project.org/package=textrecipes.
Lovins, Julie B. 1968. “Development of a Stemming Algorithm.” Mechanical Translation and Computational Linguistics 11: 22–31.
Miller, George A. 1995. “WordNet: A Lexical Database for English.” Commun. ACM 38 (11): 39–41. https://doi.org/10.1145/219717.219748.
Porter, Martin F. 1980. “An Algorithm for Suffix Stripping.” Program 14 (3): 130–37. https://doi.org/10.1108/eb046814.
Schofield, Alexandra, and David Mimno. 2016. “Comparing Apples to Apple: The Effects of Stemmers on Topic Models.” Transactions of the Association for Computational Linguistics 4: 287–300. https://doi.org/10.1162/tacl_a_00099.
Willett, P. 2006. “The Porter Stemming Algorithm: Then and Now.” Program: Electronic Library and Information Systems 40 (3): 219–23. http://eprints.whiterose.ac.uk/1434/.