Thai Chunk Parser
-In PyThaiNLP, We use chunk data from ORCHID++ corpus.
+This tutorial demonstrates how to use the chunk_parse function from the PyThaiNLP library for parsing Thai text into phrases. We will use a chunking model trained on ORCHID++ corpus.
Read more: https://github.com/PyThaiNLP/pythainlp/pull/524
+We will need the following libraries and packages: - PyThaiNLP - NLTK (to preprocess chunk data for visualization) - svgling (for visualization) - python-crfsuite
-
[5]:
+[1]:
-!pip install pythainlp svgling nltk python-crfsuite
+!pip install pythainlp svgling nltk python-crfsuite
@@ -139,50 +140,39 @@ Thai Chunk Parser
-[1]:
-
-
-from pythainlp.tokenize import word_tokenize
-from pythainlp.tag import pos_tag
-from pythainlp.tag import chunk_parse
-from nltk.chunk import conlltags2tree
-import svgling
-
-
-
+We need to import the following modules and functions: - word_tokenize – this function takes a Thai text and returns a list of tokenized words - pos_tag – this function takes a list of tokenized words and marks them with part-of-speech (POS) tags - chunk_parse – this function takes words with their POS tags and marks them with inside-outside-beginning (IOB) tags - conlltags2tree – this function is part of the NLTK and converts IOB format to a tree - svgling – this package
+will be used to visualize the tree in SVG
[2]:
-def test(txt):
- m = [(w,t) for w,t in pos_tag(word_tokenize(txt), engine= 'perceptron',corpus = 'orchid')]
- tag = chunk_parse(m)
- p = [(w,t,tag[i]) for i,(w,t) in enumerate(m)]
- return p
+from pythainlp.tokenize import word_tokenize
+from pythainlp.tag import pos_tag
+from pythainlp.tag import chunk_parse
+from nltk.chunk import conlltags2tree
+import svgling
-
+We define a new function test, which will first segment the input text into words (word_tokenize), tag the words with their parts of speech based on the ORCHID++ corpus (pos_tag) and perform chunking (chunk_parse). The function then combines the words, POS and IOB tags into a list of triples p.
+
[3]:
-svgling.draw_tree(conlltags2tree(test("แมวกินปลา")))
+def test(txt):
+ m = [(w,t) for w,t in pos_tag(word_tokenize(txt), engine= 'perceptron',corpus = 'orchid')]
+ tag = chunk_parse(m)
+ p = [(w,t,tag[i]) for i,(w,t) in enumerate(m)]
+ return p
-
-[3]:
-
-
-
-
-
+Finally, we call the test function to chunk several example sentences. We then use the svgling.draw_tree function to visualize the syntactic trees, which were generated from the chunked data by the conlltags2tree function.
[4]:
-svgling.draw_tree(conlltags2tree(test("คนหนองคายเป็นคนน่ารัก")))
+svgling.draw_tree(conlltags2tree(test("แมวกินปลา")))
@@ -191,13 +181,13 @@ Thai Chunk Parser
-
+
[5]:
-svgling.draw_tree(conlltags2tree(test("ปลาอะไรอยู่ในน้ำ")))
+svgling.draw_tree(conlltags2tree(test("คนหนองคายเป็นคนน่ารัก")))
@@ -206,13 +196,13 @@ Thai Chunk Parser
-
+
[6]:
-svgling.draw_tree(conlltags2tree(test("ในน้ำมีอะไรอยู่")))
+svgling.draw_tree(conlltags2tree(test("ปลาอะไรอยู่ในน้ำ")))
@@ -221,13 +211,13 @@ Thai Chunk Parser
-
+
[7]:
-svgling.draw_tree(conlltags2tree(test("ทำไมเขารักคุณ")))
+svgling.draw_tree(conlltags2tree(test("ในน้ำมีอะไรอยู่")))
@@ -236,13 +226,13 @@ Thai Chunk Parser
-
+
[8]:
-svgling.draw_tree(conlltags2tree(test("คนอะไรอยู่หลังต้นไม้")))
+svgling.draw_tree(conlltags2tree(test("ทำไมเขารักคุณ")))
@@ -251,16 +241,23 @@ Thai Chunk Parser
-
+
-
-[8]:
+
+[9]:
-
+svgling.draw_tree(conlltags2tree(test("คนอะไรอยู่หลังต้นไม้")))
+
+[9]:
+
+
+
+
+
diff --git a/notebooks/pythainlp_chunk.ipynb b/notebooks/pythainlp_chunk.ipynb
index 0d63a0c..931095b 100644
--- a/notebooks/pythainlp_chunk.ipynb
+++ b/notebooks/pythainlp_chunk.ipynb
@@ -8,14 +8,25 @@
"source": [
"# Thai Chunk Parser\n",
"\n",
- "In PyThaiNLP, We use chunk data from ORCHID++ corpus.\n",
+ "This tutorial demonstrates how to use the `chunk_parse` function from the PyThaiNLP library for parsing Thai text into phrases. We will use a chunking model trained on ORCHID++ corpus. \n",
"\n",
"Read more: https://github.com/PyThaiNLP/pythainlp/pull/524"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will need the following libraries and packages: \n",
+ "- PyThaiNLP\n",
+ "- NLTK (to preprocess chunk data for visualization)\n",
+ "- svgling (for visualization)\n",
+ "- python-crfsuite"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -53,9 +64,21 @@
"!pip install pythainlp svgling nltk python-crfsuite"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We need to import the following modules and functions:\n",
+ "- `word_tokenize` – this function takes a Thai text and returns a list of tokenized words\n",
+ "- `pos_tag` – this function takes a list of tokenized words and marks them with part-of-speech (POS) tags\n",
+ "- `chunk_parse` – this function takes words with their POS tags and marks them with inside-outside-beginning (IOB) tags\n",
+ "- `conlltags2tree` – this function is part of the NLTK and converts IOB format to a tree\n",
+ "- `svgling` – this package will be used to visualize the tree in SVG\n"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {
"id": "ZPRRhKxrhlFA"
},
@@ -68,9 +91,16 @@
"import svgling"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We define a new function `test`, which will first segment the input text into words (`word_tokenize`), tag the words with their parts of speech based on the ORCHID++ corpus (`pos_tag`) and perform chunking (`chunk_parse`). The function then combines the words, POS and IOB tags into a list of triples `p`."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {
"id": "bGD2uxMFhmh4"
},
@@ -83,9 +113,16 @@
" return p"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we call the `test` function to chunk several example sentences. We then use the `svgling.draw_tree` function to visualize the syntactic trees, which were generated from the chunked data by the `conlltags2tree` function."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -104,7 +141,7 @@
"TreeLayout(Tree('S', [Tree('NP', [('แมว', 'NCMN')]), Tree('VP', [('กิน', 'VACT'), ('ปลา', 'NCMN')])]))"
]
},
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -115,7 +152,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -134,7 +171,7 @@
"TreeLayout(Tree('S', [Tree('NP', [('คน', 'NCMN'), ('หนองคาย', 'NCMN')]), Tree('VP', [('เป็น', 'VSTA'), ('คน', 'NCMN'), ('น่ารัก', 'VATT')])]))"
]
},
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -145,7 +182,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -164,7 +201,7 @@
"TreeLayout(Tree('S', [Tree('NP', [('ปลา', 'NCMN'), ('อะไร', 'PNTR')]), Tree('VP', [('อยู่', 'VSTA'), ('ใน', 'RPRE'), ('น้ำ', 'NCMN')])]))"
]
},
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -175,7 +212,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -194,7 +231,7 @@
"TreeLayout(Tree('S', [Tree('NP', [('ใน', 'RPRE'), ('น้ำ', 'NCMN')]), Tree('VP', [('มี', 'VSTA'), ('อะไร', 'PNTR'), ('อยู่', 'XVAE')])]))"
]
},
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -205,7 +242,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -224,7 +261,7 @@
"TreeLayout(Tree('S', [Tree('NP', [('ทำไม', 'NCMN'), ('เขา', 'PPRS')]), Tree('VP', [('รัก', 'VACT'), ('คุณ', 'PPRS')])]))"
]
},
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -235,7 +272,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -254,7 +291,7 @@
"TreeLayout(Tree('S', [Tree('NP', [('คน', 'NCMN'), ('อะไร', 'PNTR')]), Tree('VP', [('อยู่', 'VSTA'), ('หลัง', 'RPRE'), ('ต้นไม้', 'NCMN')])]))"
]
},
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -262,15 +299,6 @@
"source": [
"svgling.draw_tree(conlltags2tree(test(\"คนอะไรอยู่หลังต้นไม้\")))"
]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "id": "SP3ZlCeQJWpq"
- },
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -278,10 +306,23 @@
"provenance": []
},
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
"name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.9"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/notebooks/pythainlp_get_started.html b/notebooks/pythainlp_get_started.html
index 1605f54..ed47ff6 100644
--- a/notebooks/pythainlp_get_started.html
+++ b/notebooks/pythainlp_get_started.html
@@ -4,7 +4,7 @@
- PyThaiNLP Get Started — pythainlp-tutorials thai2plot-18-g321b53d documentation
+ PyThaiNLP Get Started — pythainlp-tutorials thai2plot-20-gf4ed474 documentation
diff --git a/notebooks/pythainlp_wangchanberta.html b/notebooks/pythainlp_wangchanberta.html
index df4d321..ed94a32 100644
--- a/notebooks/pythainlp_wangchanberta.html
+++ b/notebooks/pythainlp_wangchanberta.html
@@ -4,7 +4,7 @@
- Wangchanberta — pythainlp-tutorials thai2plot-18-g321b53d documentation
+ Wangchanberta — pythainlp-tutorials thai2plot-20-gf4ed474 documentation
diff --git a/notebooks/sentiment_analysis.html b/notebooks/sentiment_analysis.html
index e468001..8ff2d57 100644
--- a/notebooks/sentiment_analysis.html
+++ b/notebooks/sentiment_analysis.html
@@ -4,7 +4,7 @@
- Wisesight Sentiment Analysis — pythainlp-tutorials thai2plot-18-g321b53d documentation
+ Wisesight Sentiment Analysis — pythainlp-tutorials thai2plot-20-gf4ed474 documentation
diff --git a/notebooks/spaCy_PyThaiNLP_demo.html b/notebooks/spaCy_PyThaiNLP_demo.html
index b3f7f7f..a0e1c9f 100644
--- a/notebooks/spaCy_PyThaiNLP_demo.html
+++ b/notebooks/spaCy_PyThaiNLP_demo.html
@@ -4,7 +4,7 @@
- spaCy-PyThaiNLP — pythainlp-tutorials thai2plot-18-g321b53d documentation
+ spaCy-PyThaiNLP — pythainlp-tutorials thai2plot-20-gf4ed474 documentation
diff --git a/notebooks/text_classification.html b/notebooks/text_classification.html
index 96930cf..0b12206 100644
--- a/notebooks/text_classification.html
+++ b/notebooks/text_classification.html
@@ -4,7 +4,7 @@
- Wongnai Review Classification — pythainlp-tutorials thai2plot-18-g321b53d documentation
+ Wongnai Review Classification — pythainlp-tutorials thai2plot-20-gf4ed474 documentation
diff --git a/notebooks/text_generation.html b/notebooks/text_generation.html
index c74e1bf..b74e124 100644
--- a/notebooks/text_generation.html
+++ b/notebooks/text_generation.html
@@ -4,7 +4,7 @@
- Thai Wiki Language Model for Text Generation — pythainlp-tutorials thai2plot-18-g321b53d documentation
+ Thai Wiki Language Model for Text Generation — pythainlp-tutorials thai2plot-20-gf4ed474 documentation
diff --git a/notebooks/thai_wav2vec2_onnx.html b/notebooks/thai_wav2vec2_onnx.html
index f66a982..503e149 100644
--- a/notebooks/thai_wav2vec2_onnx.html
+++ b/notebooks/thai_wav2vec2_onnx.html
@@ -4,7 +4,7 @@
- Thai Wav2vec2 model to ONNX model — pythainlp-tutorials thai2plot-18-g321b53d documentation
+ Thai Wav2vec2 model to ONNX model — pythainlp-tutorials thai2plot-20-gf4ed474 documentation
diff --git a/notebooks/wangchanberta_getting_started_aireseach.html b/notebooks/wangchanberta_getting_started_aireseach.html
index d1bdb87..dc356d7 100644
--- a/notebooks/wangchanberta_getting_started_aireseach.html
+++ b/notebooks/wangchanberta_getting_started_aireseach.html
@@ -4,7 +4,7 @@
- WangchanBERTa: Getting Started Notebook — pythainlp-tutorials thai2plot-18-g321b53d documentation
+ WangchanBERTa: Getting Started Notebook — pythainlp-tutorials thai2plot-20-gf4ed474 documentation
diff --git a/notebooks/word2vec_examples.html b/notebooks/word2vec_examples.html
index 9b50b65..ce9433f 100644
--- a/notebooks/word2vec_examples.html
+++ b/notebooks/word2vec_examples.html
@@ -4,7 +4,7 @@
- Thai2Vec Embeddings Examples — pythainlp-tutorials thai2plot-18-g321b53d documentation
+ Thai2Vec Embeddings Examples — pythainlp-tutorials thai2plot-20-gf4ed474 documentation
diff --git a/search.html b/search.html
index ec723f7..429a6fc 100644
--- a/search.html
+++ b/search.html
@@ -3,7 +3,7 @@
- Search — pythainlp-tutorials thai2plot-18-g321b53d documentation
+ Search — pythainlp-tutorials thai2plot-20-gf4ed474 documentation
diff --git a/searchindex.js b/searchindex.js
index ff1f3fc..5594404 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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