LexiconNER

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. val KNOWN_CASE_INSENSITIVE_LENGTH: Int

   

5. val OUTSIDE_LABEL: String

   

6. val OVERRIDE_ENTITY_VALIDATOR: Boolean

   

7. var USE_COMPACT: Boolean

   

   If the FastLexiconNERBuilder is beind used, indicates when true that a CompactLexiconNER should be created and otherwise a CombinedLexiconNER

8. val USE_DEBUG: Boolean

   

9. var USE_FAST: Boolean

   

   When true indicates use of the FastLexiconNERBuilder and otherwise the SlowLexiconNERBuilder to construct the LexiconNER

10. def apply(kbs: Seq[String], entityValidator: EntityValidator = new TrueEntityValidator, useLemmasForMatching: Boolean = false, caseInsensitiveMatching: Boolean = false): LexiconNER

    

    Creates a LexiconNER from a list of KBs Note that file name (minus the extension) for each KB becomes the name of the corresponding category.

    Creates a LexiconNER from a list of KBs Note that file name (minus the extension) for each KB becomes the name of the corresponding category. For example, /Some/Path/SomeCategory.tsv.gz yields the category name SomeCategory. Each of the KBs must contain one entity name per line

    kbs

    KBs containing known entity names

    entityValidator

    Filter which decides if a matched entity is valid

    useLemmasForMatching

    If true, we use Sentence.lemmas instead of Sentence.words during matching

    caseInsensitiveMatching

    If true, tokens are matched case insensitively

    returns

    The new LexiconNER

11. def apply(kbs: Seq[String], overrideKBs: Option[Seq[String]], entityValidator: EntityValidator, lexicalVariationEngine: LexicalVariations, useLemmasForMatching: Boolean, caseInsensitiveMatching: Boolean): LexiconNER

    

    Creates a LexiconNER from a list of KBs Note that file name (minus the extension) for each KB becomes the name of the corresponding category.

    Creates a LexiconNER from a list of KBs Note that file name (minus the extension) for each KB becomes the name of the corresponding category. For example, /Some/Path/SomeCategory.tsv.gz yields the category name SomeCategory. Each of the KBs must contain one entity name per line

    kbs

    KBs containing known entity names

    overrideKBs

    KBs containing override labels for entity names from kbs (necessary for the bio domain)

    entityValidator

    Filter which decides if a matched entity is valid

    lexicalVariationEngine

    Generates alternative spellings of an entity name (necessary for the bio domain)

    useLemmasForMatching

    If true, we use Sentence.lemmas instead of Sentence.words during matching

    caseInsensitiveMatching

    If true, tokens are matched case insensitively

    returns

    The new LexiconNER

12. def apply(kbs: Seq[String], caseInsensitiveMatchings: Seq[Boolean], baseDirOpt: Option[File]): LexiconNER

    

    This is just like the above but with the addition of the baseDirOpt.

13. def apply(kbs: Seq[String], caseInsensitiveMatchings: Seq[Boolean]): LexiconNER

    

    Same apply with even more default values filled in

14. def apply(kbs: Seq[String], caseInsensitiveMatchings: Seq[Boolean], useLemmasForMatching: Boolean): LexiconNER

    

    Same apply with more default values filled in

15. def apply(kbs: Seq[String], caseInsensitiveMatchings: Seq[Boolean], entityValidator: EntityValidator, useLemmasForMatching: Boolean): LexiconNER

    

    Same apply with some default values filled in

16. def apply(kbs: Seq[String], overrideKBs: Option[Seq[String]], caseInsensitiveMatchings: Seq[Boolean], entityValidator: EntityValidator, lexicalVariationEngine: LexicalVariations, useLemmasForMatching: Boolean, defaultCaseInsensitive: Boolean, baseDirOpt: Option[File]): LexiconNER

    

    Create a LexiconNER from a pair of sequences of knowledge bases (KBs), the kbs and overrideKBs, with control over the case sensitivity of individual KBs via caseInsensitiveMatchings

    Create a LexiconNER from a pair of sequences of knowledge bases (KBs), the kbs and overrideKBs, with control over the case sensitivity of individual KBs via caseInsensitiveMatchings

    The matchings run parallel to the KBs. That is, caseInsensitiveMatchings(n) is used for kbs(n). It is possible that contents of an overrideKB refers to a KB that does not exist. In that situation, caseInsensitiveMatching is used as a fallback value.

    kbs

    KBs containing known entity names

    overrideKBs

    KBs containing override labels for entity names from kbs (necessary for the bio domain)

    caseInsensitiveMatchings

    case insensitivities corresponding to the kbs, matched by index

    entityValidator

    Filter which decides if a matched entity is valid

    lexicalVariationEngine

    Generates alternative spellings of an entity name (necessary for the bio domain)

    useLemmasForMatching

    If true, we use Sentence.lemmas instead of Sentence.words during matching

    defaultCaseInsensitive

    If true, tokens are matched case insensitively

    baseDirOpt

    An optional directory to force kbs to be loaded from files rather than resources

    returns

    The new LexiconNER

17. def apply(standardKbSources: Seq[StandardKbSource], overrideKbSourcesOpt: Option[Seq[OverrideKbSource]], lexicalVariationEngine: LexicalVariations, entityValidator: EntityValidator, useLemmasForMatching: Boolean, defaultCaseInsensitive: Boolean): LexiconNER

    

    Create a LexiconNER from a pair of sequences of knowledge base sources for the kbs and overrideKBs.

    Create a LexiconNER from a pair of sequences of knowledge base sources for the kbs and overrideKBs. There are versions of the sources for knowledge bases stored in files and those stored in memory. Each StandardKbSource knows its own caseSensitivityMatching, so no list of those need be supplied. It is possible that contents of an overrideKB refers to a KB (label) that does not exist. In that situation, caseInsensitiveMatching is used as a fallback value. With that, this method should encompass all the functionality of the other apply methods, which now feed into it. Note that some of the arugments are in a different order than the other build methods in order to overload the method despite type erasure.

    standardKbSources

    KB sources containing known entity names

    overrideKbSourcesOpt

    KB sources containing override labels for entity names from kbs (necessary for the bio domain)

    lexicalVariationEngine

    Generates alternative spellings of an entity name (necessary for the bio domain)

    entityValidator

    Filter which decides if a matched entity is valid

    useLemmasForMatching

    If true, we use Sentence.lemmas instead of Sentence.words during matching

    defaultCaseInsensitive

    If true, tokens are matched case insensitively

    returns

    The new LexiconNER

18. final def asInstanceOf[T0]: T0

    

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19. def clone(): AnyRef

    

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20. def countWhile(labels: Array[String], offset: Int, condition: (String) ⇒ Boolean): Int

    

21. final def eq(arg0: AnyRef): Boolean

    

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22. def equals(arg0: Any): Boolean

    

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23. def finalize(): Unit

    

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24. final def getClass(): Class[_]

    

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25. def hashCode(): Int

    

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26. final def isInstanceOf[T0]: Boolean

    

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27. def isNotOutside(label: String): Boolean

    

28. def isOutside(label: String): Boolean

    

29. def mergeLabels(dst: Array[String], src: Array[String]): Unit

    

    Merges labels from src into dst without overlapping any existing labels in dst.

30. final def ne(arg0: AnyRef): Boolean

    

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31. final def notify(): Unit

    

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32. final def notifyAll(): Unit

    

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33. def scanText(words: Array[String], start: Int, end: Int): (Int, Int, Int, Int, Int)

    

34. final def synchronized[T0](arg0: ⇒ T0): T0

    

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35. def toString(): String

    

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36. final def wait(): Unit

    

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37. final def wait(arg0: Long, arg1: Int): Unit

    

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38. final def wait(arg0: Long): Unit

    

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