108 lines
3.9 KiB
Properties
108 lines
3.9 KiB
Properties
# The properties in this file will be applied as defaults.
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# A custom properties file should only should override or add the properties it is interested in.
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# -----------------------
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# General and fixed flags
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# -----------------------
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patternType = SURFACE
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# To ignore case differences within the text
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lowercaseText = true
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# This ignores spelling mistakes (common for social media),
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# but is slow
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#fuzzyMatch = true
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minLen4FuzzyForPattern = 4
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# Do not evaluate (broken for our purposes)
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evaluate = false
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evalPerEntity = false
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# These two are assumed to have this value
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useTargetParserParentRestriction = false
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useTargetNERRestriction = true
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# The application doesn't check that the patterns are relevant to the corpus.
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# Don't store, always compute them
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computeAllPatterns = true
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# Do not use Lucene or a RDBMS for temporary storage
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storePatsForEachToken = MEMORY
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# = save model and results.
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# Needed for TextProc
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savePatternsWordsDir = true
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# This property is needed for file saving to work
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identifier = identifier
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# Using regexner and gazetteers makes the entity extraction process
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# 10 times slower and much more memory intensive
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applyFineGrainedRegexner = false
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# --------------------------------------------------------------------------------------------------------------------------
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# Pattern flags
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# (copied from https://github.com/stanfordnlp/CoreNLP/blob/master/data/edu/stanford/nlp/patterns/surface/example.properties)
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# --------------------------------------------------------------------------------------------------------------------------
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# ***use context on the left
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usePreviousContext = true
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# ***use context on the right
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useNextContext = true
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# ***the context should be at least this long
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minWindow4Pattern = 2
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# ***the context can be at most this long
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maxWindow4Pattern = 4
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# if the context consists of only stop words, add only if it's more than these many stop words
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numMinStopWordsToAdd = 3
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# ***use POS tag restriction for the target phrase
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usePOS4Pattern = true
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# Ignore words {a, an, the} while matching the patterns to text (advisable true)
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useFillerWordsInPat = false
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# If your code is running too slow, try to reduce this number. Samples % of sentences for learning patterns
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sampleSentencesForSufficientStats = 1.0
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# maximum number of allowed words in the target phrase
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numWordsCompound = 3
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# consider patterns without the POS restriction on the target phrase
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addPatWithoutPOS = true
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# Ignore common stop words occurring just before the target phrase
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useStopWordsBeforeTerm = false
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# Use lemma instead of words of the context tokens
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useLemmaContextTokens = true
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# make context matching lowercase (advisable)
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matchLowerCaseContext = true
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# use named entity tag restrictions for the context (neighboring) tokens
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useContextNERRestriction = false
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# do not extract phrase in which any word is labeled with another class
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# (for example, you don't wanna extract 'HIV patients' as disease)
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doNotExtractPhraseAnyWordLabeledOtherClass = true
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# kinda ignore this flag and use it as true. for those who care this too much: for each token,
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# we use the phrase that originally matched that token instead of the token's word
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# (in case you are using fuzzy matching)
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useMatchingPhrase = true
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# Use only the tokens that get matched by a pattern (advisable as false)
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restrictToMatched = false
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# Label the learned words in the text (advisable as true)
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usePatternResultAsLabel = true
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# Words excluded from NER labeling
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englishWordsFiles = data/edu/stanford/nlp/patterns/surface/stopwords.txt
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# Words to be ignored when learning phrases.
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# See the commonWordsPatternFiles field in the ConstantsAndVariables class
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commonWordsPatternFiles = data/edu/stanford/nlp/patterns/surface/stopwords.txt
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# remove common stop words from phrases to get clean phrases (for example, "disease" instead of "some disease")
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removeStopWordsFromSelectedPhrases = true
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# Do not learn phrases that have any stop word
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removePhrasesWithStopWords = false
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