/// <summary>
/// Return the context for finding names at the specified index.
/// </summary>
/// <param name="index">
/// The index of the token in the specified tokens array for which the context should be constructed.
/// </param>
/// <param name="tokens">
/// tokens of the sentence.
/// </param>
/// <param name="predicates">
/// The previous decisions made in the tagging of this sequence. Only indices less than {index} will be examined.
/// </param>
/// <param name="previousTags">
/// A mapping between tokens and the previous outcome for these tokens.
/// </param>
/// <returns>
/// the context for finding names at the specified index.
/// </returns>
public virtual string[] GetContext(int index, string[] tokens, string[] predicates, IDictionary <string, string> previousTags)
{
string previous = MaximumEntropyNameFinder.Other;
string previousPrevious = MaximumEntropyNameFinder.Other;
if (index > 1)
{
previousPrevious = predicates[index - 2];
}
if (index > 0)
{
previous = predicates[index - 1];
}
string cacheKey = index.ToString(System.Globalization.CultureInfo.InvariantCulture) + previous + previousPrevious;
if (mContextsCache != null)
{
if (mWordsKey == tokens)
{
string[] cachedContexts = (string[])mContextsCache[cacheKey];
if (cachedContexts != null)
{
return(cachedContexts);
}
}
else
{
mContextsCache.Clear();
mWordsKey = tokens;
}
}
List <string> features;
if (mWordsKey == tokens && index == mPreviousIndex)
{
features = mPreviousStaticFeatures;
}
else
{
features = GetStaticFeatures(tokens, index, previousTags);
mPreviousIndex = index;
mPreviousStaticFeatures = features;
}
int featureCount = features.Count;
string[] contexts = new string[featureCount + 4];
for (int currentFeature = 0; currentFeature < featureCount; currentFeature++)
{
contexts[currentFeature] = features[currentFeature];
}
contexts[featureCount] = "po=" + previous;
contexts[featureCount + 1] = "pow=" + previous + tokens[index];
contexts[featureCount + 2] = "powf=" + previous + WordFeature(tokens[index]);
contexts[featureCount + 3] = "ppo=" + previousPrevious;
if (mContextsCache != null)
{
mContextsCache[cacheKey] = contexts;
}
return(contexts);
}
/// <summary>
/// Returns the contexts for chunking of the specified index.
/// </summary>
/// <param name="index">
/// The index of the token in the specified tokens array for which the context should be constructed.
/// </param>
/// <param name="words">
/// The tokens of the sentence. The <code>ToString()</code> methods of these objects should return the token text.
/// </param>
/// <param name="predicates">
/// The previous decisions made in the tagging of this sequence. Only indices less than i will be examined.
/// </param>
/// <param name="tags">
/// The POS tags for the the specified tokens.
/// </param>
/// <returns>
/// An array of predictive contexts on which a model basis its decisions.
/// </returns>
public virtual string[] GetContext(int index, object[] words, string[] tags, string[] predicates)
{
List <string> features = new List <string>(19);
int currentTokenIndex = index;
int previousPreviousTokenIndex = currentTokenIndex - 2;
int previousTokenIndex = currentTokenIndex - 1;
int nextNextTokenIndex = currentTokenIndex + 2;
int nextTokenIndex = currentTokenIndex + 1;
string previousPreviousWord, previousWord, currentWord, nextWord, nextNextWord;
string previousPreviousTag, previousTag, currentTag, nextTag, nextNextTag;
string previousPreviousPriorDecision, previousPriorDecision;
string[] contexts;
// ChunkAndPosTag(-2)
if (previousPreviousTokenIndex >= 0)
{
previousPreviousTag = tags[previousPreviousTokenIndex];
previousPreviousPriorDecision = predicates[previousPreviousTokenIndex];
previousPreviousWord = words[previousPreviousTokenIndex].ToString();
}
else
{
previousPreviousTag = mEndOfSentence;
previousPreviousPriorDecision = mEndOfSentence;
previousPreviousWord = mEndOfSentence;
}
// ChunkAndPosTag(-1)
if (previousTokenIndex >= 0)
{
previousTag = tags[previousTokenIndex];
previousPriorDecision = predicates[previousTokenIndex];
previousWord = words[previousTokenIndex].ToString();
}
else
{
previousTag = mEndOfSentence;
previousPriorDecision = mEndOfSentence;
previousWord = mEndOfSentence;
}
// ChunkAndPosTag(0)
currentTag = tags[currentTokenIndex];
currentWord = words[currentTokenIndex].ToString();
// ChunkAndPosTag(1)
if (nextTokenIndex < tags.Length)
{
nextTag = tags[nextTokenIndex];
nextWord = words[nextTokenIndex].ToString();
}
else
{
nextTag = mEndOfSentence;
nextWord = mEndOfSentence;
}
// ChunkAndPosTag(2)
if (nextNextTokenIndex < tags.Length)
{
nextNextTag = tags[nextNextTokenIndex];
nextNextWord = words[nextNextTokenIndex].ToString();
}
else
{
nextNextTag = mEndOfSentence;
nextNextWord = mEndOfSentence;
}
string cacheKey = currentTokenIndex.ToString(System.Globalization.CultureInfo.InvariantCulture) + previousPreviousTag + previousTag
+ currentTag + nextTag + nextNextTag + previousPreviousPriorDecision
+ previousPriorDecision;
if (mContextsCache != null)
{
if (mWordsKey == words)
{
contexts = (string[])mContextsCache[cacheKey];
if (contexts != null)
{
return(contexts);
}
}
else
{
mContextsCache.Clear();
mWordsKey = words;
}
}
string previousPreviousChunkTag = ChunkAndPosTag(-2, previousPreviousWord, previousPreviousTag, previousPreviousPriorDecision);
string previousPreviousChunkTagBackOff = ChunkAndPosTagBackOff(-2, previousPreviousTag, previousPreviousPriorDecision);
string previousChunkTag = ChunkAndPosTag(-1, previousWord, previousTag, previousPriorDecision);
string previousChunkTagBackOff = ChunkAndPosTagBackOff(-1, previousTag, previousPriorDecision);
string currentChunkTag = ChunkAndPosTag(0, currentWord, currentTag, null);
string currentChunkTagBackOff = ChunkAndPosTagBackOff(0, currentTag, null);
string nextChunkTag = ChunkAndPosTag(1, nextWord, nextTag, null);
string nextChunkTagBackOff = ChunkAndPosTagBackOff(1, nextTag, null);
string nextNextChunkTag = ChunkAndPosTag(2, nextNextWord, nextNextTag, null);
string nextNextChunkTagBackOff = ChunkAndPosTagBackOff(2, nextNextTag, null);
features.Add("default");
features.Add(previousPreviousChunkTag);
features.Add(previousPreviousChunkTagBackOff);
features.Add(previousChunkTag);
features.Add(previousChunkTagBackOff);
features.Add(currentChunkTag);
features.Add(currentChunkTagBackOff);
features.Add(nextChunkTag);
features.Add(nextChunkTagBackOff);
features.Add(nextNextChunkTag);
features.Add(nextNextChunkTagBackOff);
//ChunkAndPosTag(-1,0)
features.Add(previousChunkTag + "," + currentChunkTag);
features.Add(previousChunkTagBackOff + "," + currentChunkTag);
features.Add(previousChunkTag + "," + currentChunkTagBackOff);
features.Add(previousChunkTagBackOff + "," + currentChunkTagBackOff);
//ChunkAndPosTag(0,1)
features.Add(currentChunkTag + "," + nextChunkTag);
features.Add(currentChunkTagBackOff + "," + nextChunkTag);
features.Add(currentChunkTag + "," + nextChunkTagBackOff);
features.Add(currentChunkTagBackOff + "," + nextChunkTagBackOff);
contexts = features.ToArray();
if (mContextsCache != null)
{
mContextsCache[cacheKey] = contexts;
}
return(contexts);
}
/// <summary>
/// Returns the context for making a pos tag decision at the specified token index given the specified tokens and previous tags.
/// </summary>
/// <param name="index">
/// The index of the token for which the context is provided.
/// </param>
/// <param name="tokens">
/// The tokens in the sentence.
/// </param>
/// <param name="tags">
/// The tags assigned to the previous words in the sentence.
/// </param>
/// <returns>
/// The context for making a pos tag decision at the specified token index given the specified tokens and previous tags.
/// </returns>
public virtual string[] GetContext(int index, string[] tokens, string[] tags)
{
string next, nextNext, lex, previous, previousPrevious;
string tagPrevious, tagPreviousPrevious;
tagPrevious = tagPreviousPrevious = null;
next = nextNext = lex = previous = previousPrevious = null;
lex = tokens[index];
if (tokens.Length > index + 1)
{
next = tokens[index + 1];
if (tokens.Length > index + 2)
{
nextNext = tokens[index + 2];
}
else
{
nextNext = SentenceEnd;
}
}
else
{
next = SentenceEnd;
}
if (index - 1 >= 0)
{
previous = tokens[index - 1];
tagPrevious = tags[index - 1];
if (index - 2 >= 0)
{
previousPrevious = tokens[index - 2];
tagPreviousPrevious = tags[index - 2];
}
else
{
previousPrevious = SentenceBeginning;
}
}
else
{
previous = SentenceBeginning;
}
string cacheKey = index.ToString(System.Globalization.CultureInfo.InvariantCulture) + tagPrevious + tagPreviousPrevious;
if (_contextsCache != null)
{
if (_wordsKey == tokens)
{
var cachedContexts = (string[])_contextsCache[cacheKey];
if (cachedContexts != null)
{
return(cachedContexts);
}
}
else
{
_contextsCache.Clear();
_wordsKey = tokens;
}
}
var eventList = new List <string>();
// add the word itself
eventList.Add("w=" + lex);
// do some basic suffix analysis
string[] suffixes = GetSuffixes(lex);
for (int currentSuffix = 0; currentSuffix < suffixes.Length; currentSuffix++)
{
eventList.Add("suf=" + suffixes[currentSuffix]);
}
string[] prefixes = GetPrefixes(lex);
for (int currentPrefix = 0; currentPrefix < prefixes.Length; currentPrefix++)
{
eventList.Add("pre=" + prefixes[currentPrefix]);
}
// see if the word has any special characters
if (lex.IndexOf('-') != -1)
{
eventList.Add("h");
}
if (HasCapitalRegex.IsMatch(lex))
{
eventList.Add("c");
}
if (HasNumericRegex.IsMatch(lex))
{
eventList.Add("d");
}
// add the words and positions of the surrounding context
if ((object)previous != null)
{
eventList.Add("p=" + previous);
if ((object)tagPrevious != null)
{
eventList.Add("t=" + tagPrevious);
}
if ((object)previousPrevious != null)
{
eventList.Add("pp=" + previousPrevious);
if ((object)tagPreviousPrevious != null)
{
eventList.Add("tt=" + tagPreviousPrevious);
}
}
}
if ((object)next != null)
{
eventList.Add("n=" + next);
if ((object)nextNext != null)
{
eventList.Add("nn=" + nextNext);
}
}
string[] contexts = eventList.ToArray();
if (_contextsCache != null)
{
_contextsCache[cacheKey] = contexts;
}
return(contexts);
}