Upgrade matching logic to BM25 search scoring with Soundex phonetic corrector, bigrams, and suffix stemming
This commit is contained in:
Shanmuga Krishnan S M
@@ -37,8 +37,9 @@ var (
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faqs []FAQ
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indexedDocs []IndexedDoc
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idf map[string]float64
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docVectors []map[string]float64
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docNorms []float64
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docTerms []map[string]float64 // term frequency for each document
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docLengths []float64 // length of each document
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avgDocLength float64
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vocabSet map[string]bool
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)
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@@ -97,6 +98,101 @@ var stopwords = map[string]bool{
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var cleanRegex = regexp.MustCompile(`[^a-z0-9\s]`)
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func stem(word string) string {
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if strings.HasSuffix(word, "sses") {
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return word[:len(word)-2]
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}
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if strings.HasSuffix(word, "ies") {
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return word[:len(word)-3] + "y"
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}
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if strings.HasSuffix(word, "ss") {
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return word
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}
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if strings.HasSuffix(word, "s") && !strings.HasSuffix(word, "us") && !strings.HasSuffix(word, "as") && !strings.HasSuffix(word, "is") {
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return word[:len(word)-1]
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}
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if strings.HasSuffix(word, "eed") {
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return word[:len(word)-1]
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}
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if strings.HasSuffix(word, "ing") {
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w := word[:len(word)-3]
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if len(w) > 3 && w[len(w)-1] == w[len(w)-2] {
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c := w[len(w)-1]
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if c == 'n' || c == 't' || c == 'p' || c == 'd' || c == 'g' {
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w = w[:len(w)-1]
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}
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}
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return w
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}
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if strings.HasSuffix(word, "ed") {
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return word[:len(word)-2]
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}
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if strings.HasSuffix(word, "ly") {
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return word[:len(word)-2]
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}
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if strings.HasSuffix(word, "tional") {
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return word[:len(word)-6] + "tion"
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}
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return word
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}
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func getBigrams(tokens []string) []string {
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var bigrams []string
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for i := 0; i < len(tokens)-1; i++ {
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bigrams = append(bigrams, tokens[i]+"_"+tokens[i+1])
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}
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return bigrams
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}
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func soundex(word string) string {
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if len(word) == 0 {
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return ""
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}
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word = strings.ToLower(word)
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first := string(word[0])
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mappings := map[rune]rune{
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'b': '1', 'f': '1', 'p': '1', 'v': '1',
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'c': '2', 'g': '2', 'j': '2', 'k': '2', 'q': '2', 's': '2', 'x': '2', 'z': '2',
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'd': '3', 't': '3',
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'l': '4',
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'm': '5', 'n': '5',
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'r': '6',
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}
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var code []rune
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code = append(code, rune(first[0]))
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prevCode := '0'
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if c, exists := mappings[rune(word[0])]; exists {
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prevCode = c
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}
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for i := 1; i < len(word); i++ {
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r := rune(word[i])
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if r == 'a' || r == 'e' || r == 'i' || r == 'o' || r == 'u' || r == 'y' || r == 'h' || r == 'w' {
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continue
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}
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if c, exists := mappings[r]; exists {
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if c != prevCode {
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code = append(code, c)
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prevCode = c
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}
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} else {
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prevCode = '0'
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}
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if len(code) == 4 {
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break
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}
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}
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for len(code) < 4 {
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code = append(code, '0')
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}
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return string(code)
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}
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func tokenize(text string) []string {
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text = strings.ToLower(text)
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text = cleanRegex.ReplaceAllString(text, " ")
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@@ -107,6 +203,8 @@ func tokenize(text string) []string {
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if syn, exists := synonymMap[word]; exists {
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word = syn
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}
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// Apply stemming
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word = stem(word)
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if !stopwords[word] && len(word) > 1 {
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tokens = append(tokens, word)
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}
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@@ -160,6 +258,15 @@ func correctToken(token string) string {
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return token
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}
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// Try Soundex first (phonetic correction)
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tokenSx := soundex(token)
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for vocabWord := range vocabSet {
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if soundex(vocabWord) == tokenSx {
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return vocabWord
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}
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}
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// Fallback to Levenshtein distance
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bestWord := token
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bestDist := 999
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@@ -199,7 +306,7 @@ func tokenizeQuery(query string) []string {
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return corrected
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}
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func initTFIDF() {
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func initTFIDF() { // keeping name initTFIDF for backwards compatibility
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// Build indexedDocs
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indexedDocs = nil
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for i, faq := range faqs {
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@@ -213,20 +320,37 @@ func initTFIDF() {
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}
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}
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docFrequencies := make(map[string]int)
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allTokens := make([][]string, len(indexedDocs))
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var totalLength float64
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for i, doc := range indexedDocs {
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faq := faqs[doc.FAQIndex]
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docText := doc.Question + " " + strings.Join(faq.Keywords, " ")
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tokens := tokenize(docText)
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bigrams := getBigrams(tokens)
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tokens = append(tokens, bigrams...)
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allTokens[i] = tokens
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totalLength += float64(len(tokens))
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}
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avgDocLength = totalLength / float64(len(indexedDocs))
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docFrequencies := make(map[string]int)
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docTerms = make([]map[string]float64, len(indexedDocs))
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docLengths = make([]float64, len(indexedDocs))
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for i, tokens := range allTokens {
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docLengths[i] = float64(len(tokens))
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tfMap := make(map[string]float64)
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for _, token := range tokens {
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tfMap[token]++
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}
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docTerms[i] = tfMap
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uniqueTokens := make(map[string]bool)
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for _, token := range tokens {
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uniqueTokens[token] = true
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}
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for token := range uniqueTokens {
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docFrequencies[token]++
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}
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@@ -240,37 +364,18 @@ func initTFIDF() {
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}
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}
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// Calculate IDF for each term
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// Calculate BM25 IDF for each term
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idf = make(map[string]float64)
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numDocs := float64(len(indexedDocs))
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for token, df := range docFrequencies {
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idf[token] = math.Log(1.0 + (numDocs / float64(df)))
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val := (numDocs - float64(df) + 0.5) / (float64(df) + 0.5)
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if val < 0 {
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val = 0.0001
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}
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idf[token] = math.Log(val + 1.0)
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}
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// Compute TF-IDF vectors for indexed docs
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docVectors = make([]map[string]float64, len(indexedDocs))
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docNorms = make([]float64, len(indexedDocs))
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for i, tokens := range allTokens {
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tfMap := make(map[string]float64)
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for _, token := range tokens {
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tfMap[token]++
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}
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vector := make(map[string]float64)
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var sqSum float64
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for token, tf := range tfMap {
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tfidfVal := tf * idf[token]
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vector[token] = tfidfVal
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sqSum += tfidfVal * tfidfVal
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}
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docVectors[i] = vector
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docNorms[i] = math.Sqrt(sqSum)
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}
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log.Printf("TF-IDF Chatbot engine initialized successfully with %d Q&As (%d indexed question variants).", len(faqs), len(indexedDocs))
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log.Printf("BM25 Chatbot engine initialized successfully with %d Q&As (%d indexed question variants, avg doc length: %.2f).", len(faqs), len(indexedDocs), avgDocLength)
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}
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func getBestMatch(query string) (int, float64, string) {
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@@ -279,61 +384,47 @@ func getBestMatch(query string) (int, float64, string) {
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return -1, 0.0, ""
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}
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// Compute TF for query
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queryTF := make(map[string]float64)
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for _, token := range queryTokens {
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queryTF[token]++
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}
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// Compute TF-IDF vector for query
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queryVector := make(map[string]float64)
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var querySqSum float64
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for token, tf := range queryTF {
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if idfVal, exists := idf[token]; exists {
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tfidfVal := tf * idfVal
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queryVector[token] = tfidfVal
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querySqSum += tfidfVal * tfidfVal
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}
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}
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queryNorm := math.Sqrt(querySqSum)
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if queryNorm == 0 {
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return -1, 0.0, ""
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}
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qBigrams := getBigrams(queryTokens)
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queryTokens = append(queryTokens, qBigrams...)
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bestFAQIdx := -1
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bestScore := -1.0
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var bestMatchedQuestion string
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for i, docVector := range docVectors {
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doc := indexedDocs[i]
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k1 := 1.2
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b := 0.75
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for i, doc := range indexedDocs {
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faq := faqs[doc.FAQIndex]
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var bm25Score float64
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var dotProduct float64
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for token, qVal := range queryVector {
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if dVal, exists := docVector[token]; exists {
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dotProduct += qVal * dVal
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tfMap := docTerms[i]
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docLen := docLengths[i]
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for _, token := range queryTokens {
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tf := tfMap[token]
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if tf > 0 {
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idfVal := idf[token]
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numerator := tf * (k1 + 1.0)
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denominator := tf + k1*(1.0-b+b*(docLen/avgDocLength))
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bm25Score += idfVal * (numerator / denominator)
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}
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}
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var cosineSim float64
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if docNorms[i] > 0 {
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cosineSim = dotProduct / (queryNorm * docNorms[i])
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}
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// Apply exact keyword boosting
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// Apply exact keyword boosting (only on original tokens)
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originalQueryLen := len(queryTokens) - len(qBigrams)
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keywordMatches := 0
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for _, kw := range faq.Keywords {
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for _, qTok := range queryTokens {
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for j := 0; j < originalQueryLen; j++ {
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qTok := queryTokens[j]
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if strings.ToLower(kw) == qTok {
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keywordMatches++
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}
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}
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}
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// A boost of 0.15 for each exact keyword match
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boost := float64(keywordMatches) * 0.15
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score := cosineSim + boost
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score := bm25Score + boost
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if score > bestScore {
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bestScore = score
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