Aho-Corasick ============ [](https://travis-ci.org/robert-bor/aho-corasick) [](https://www.codacy.com/app/bor-robert/aho-corasick) [](https://codecov.io/gh/robert-bor/aho-corasick) [](https://maven-badges.herokuapp.com/maven-central/org.ahocorasick/ahocorasick) [](http://www.javadoc.io/doc/org.ahocorasick/ahocorasick) [](http://www.apache.org/licenses/LICENSE-2.0) Dependency ---------- Include this dependency in your POM. Be sure to check for the latest version in Maven Central. ```xml <dependency> <groupId>org.ahocorasick</groupId> <artifactId>ahocorasick</artifactId> <version>0.5.0</version> </dependency> ``` Introduction ------------ Most free-text searching is based on Lucene-like approaches, where the search text is parsed into its various components. For every keyword a lookup is done to see where it occurs. When looking for a couple of keywords this approach is great, but when searching for 100,000 words, the approach is quite slow (for example, checking against a dictionary). The Aho-Corasick algorithm shines when looking for multiple words. Rather than chop up the search text, it uses all the keywords to build a [Trie](http://en.wikipedia.org/wiki/Trie) construct. The crucial Aho-Corasick components include: * goto * fail * output Every character encountered is presented to a state object within the *goto* structure. If there is a matching state, that will be elevated to the new current state. However, if there is no matching state, the algorithm will signal a *fail* and fall back to states with less depth (i.e., a match less long) and proceed from there, until it found a matching state, or it has reached the root state. Whenever a state is reached that matches an entire keyword, it is emitted to an *output* set which can be read after the entire scan has completed. The algorithm is O(n). No matter how many keywords are given, or how large the search text is, the performance will decline linearly. The Aho-Corasick algorithm can help: * find words in texts to link or emphasize them; * add semantics to plain text; or * check against a dictionary to see if syntactic errors were made. See the [white paper](http://cr.yp.to/bib/1975/aho.pdf) by Aho and Corasick for algorithmic details. Usage ----- Set up the Trie using a builder as follows: ```java Trie trie = Trie.builder() .addKeyword("hers") .addKeyword("his") .addKeyword("she") .addKeyword("he") .build(); Collection<Emit> emits = trie.parseText("ushers"); ``` The collection will contain `Emit` objects that match: * "she" starting at position 1, ending at position 3 * "he" starting at position 2, ending at position 3 * "hers" starting at position 2, ending at position 5 In situations where overlapping instances are not desired, retain the longest and left-most matches by calling `ignoreOverlaps()`: ```java Trie trie = Trie.builder() .ignoreOverlaps() .addKeyword("hot") .addKeyword("hot chocolate") .build(); Collection<Emit> emits = trie.parseText("hot chocolate"); ``` The `ignoreOverlaps()` method tells the Trie to remove all overlapping matches. For this it relies on the following conflict resolution rules: 1. longer matches prevail over shorter matches; and 1. left-most prevails over right-most. Only one result is returned: * "hot chocolate" starting at position 0, ending at position 12 To check for whole words exclusively, call `onlyWholeWords()` as follows: ```java Trie trie = Trie.builder() .onlyWholeWords() .addKeyword("sugar") .build(); Collection<Emit> emits = trie.parseText("sugarcane sugar canesugar"); ``` Only one match is found; whereas, without calling `onlyWholeWords()` four matches are found. The sugarcane/canesugar words are discarded because they are partial matches. Some text is `WrItTeN` in mixed case, which makes it hard to identify. Instruct the Trie to convert the searchtext to lowercase to ease the matching process. The lower-casing applies to keywords as well. ```java Trie trie = Trie.builder() .ignoreCase() .addKeyword("casing") .build(); Collection<Emit> emits = trie.parseText("CaSiNg"); ``` Normally, this match would not be found. By calling `ignoreCase()`, the entire search text is made lowercase before matching begins. Therefore it will find exactly one match. It is also possible to just ask whether the text matches any of the keywords, or just to return the first match it finds. ```java Trie trie = Trie.builder().ignoreOverlaps() .addKeyword("ab") .addKeyword("cba") .addKeyword("ababc") .build(); Emit firstMatch = trie.firstMatch("ababcbab"); ``` The value for `firstMatch` will be "ababc" from position 0. The `containsMatch()` method checks whether `firstMatch` found a match and returns `true` if that is the case. For a barebones Aho-Corasick algorithm with a custom emit handler use: ```java Trie trie = Trie.builder() .addKeyword("hers") .addKeyword("his") .addKeyword("she") .addKeyword("he") .build(); final List<Emit> emits = new ArrayList<>(); EmitHandler emitHandler = new EmitHandler() { @Override public void emit(Emit emit) { emits.add(emit); } }; ``` In many cases you may want to do perform tasks with both the non-matching and the matching text. Such implementations may be better served by using `Trie.tokenize()`. The `tokenize()` method allows looping over the corpus to deal with matches as soon as they are encountered. Here's an example that outputs key words as italicized HTML elements: ```java String speech = "The Answer to the Great Question... Of Life, " + "the Universe and Everything... Is... Forty-two,' said " + "Deep Thought, with infinite majesty and calm."; Trie trie = Trie.builder().ignoreOverlaps().onlyWholeWords().ignoreCase() .addKeyword("great question") .addKeyword("forty-two") .addKeyword("deep thought") .build(); Collection<Token> tokens = trie.tokenize(speech); StringBuilder html = new StringBuilder(); html.append("<html><body><p>"); for (Token token : tokens) { if (token.isMatch()) { html.append("<i>"); } html.append(token.getFragment()); if (token.isMatch()) { html.append("</i>"); } } html.append("</p></body></html>"); System.out.println(html); ``` You can also emit custom outputs. This might for example be useful to implement a trivial named entity recognizer. In this case use a `PayloadTrie` instead of a `Trie` as follows: ```java class Word { private final String gender; public Word(String gender) { this.gender = gender; } } PayloadTrie<Word> trie = PayloadTrie.<Word>builder() .addKeyword("hers", new Word("f") .addKeyword("his", new Word("m")) .addKeyword("she", new Word("f")) .addKeyword("he", new Word("m")) .addKeyword("nonbinary", new Word("nb")) .addKeyword("transgender", new Word("tg")) .build(); Collection<PayloadEmit<Word>> emits = trie.parseText("ushers"); ``` Releases -------- See [releases](https://github.com/robert-bor/aho-corasick/releases) for details.