使用widget编写js代码实现实体识别和关系抽取
时间: 2024-05-16 19:13:48 浏览: 158
要实现实体识别和关系抽取,可以使用自然语言处理工具NLTK和SpaCy。在使用这些工具之前,需要先安装它们并下载相应的模型。
以下是一个基本的JavaScript代码示例,使用widget库来实现实体识别和关系抽取:
```javascript
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<title>Entity Recognition and Relation Extraction</title>
<script src="https://cdn.jsdelivr.net/npm/@widgetjs/widget@4.2.1"></script>
<script src="https://unpkg.com/@tensorflow/tfjs@1.8.3"></script>
<script src="https://unpkg.com/@tensorflow-models/posenet@2.2.1/dist/posenet.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/speech-commands@0.5.2/dist/speech-commands.min.js">
</script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/universal-sentence-encoder@4.2.0/dist/universal-sentence-encoder.min.js">
</script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/knn-classifier@3.2.0/dist/knn-classifier.min.js">
</script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/qna@0.3.0/dist/qna.min.js">
</script>
<script src="https://unpkg.com/@tensorflow-models/blazeface@0.3.0"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/handpose@0.9.3/dist/handpose.min.js">
</script>
<script src="https://unpkg.com/@tensorflow-models/coco-ssd@2.0.0"></script>
<script src="https://unpkg.com/@tensorflow-models/mobilenet@2.0.4"></script>
<script src="https://unpkg.com/@tensorflow-models/toxicity@1.4.1"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/body-pix@2.0.3/dist/body-pix.min.js">
</script>
<script src="https://unpkg.com/@tensorflow-models/facemesh@0.1.5/dist/facemesh.min.js">
</script>
<script src="https://unpkg.com/@tensorflow-models/human-pose-estimation@2.2.0"></script>
</head>
<body>
<div id="input">
<textarea id="input-text" rows="5" cols="50" placeholder="Enter text here"></textarea>
<button id="submit-button">Submit</button>
</div>
<div id="output">
<div id="entities"></div>
<div id="relations"></div>
</div>
<script>
let entitiesDiv = document.getElementById("entities");
let relationsDiv = document.getElementById("relations");
async function recognizeEntities(text) {
let spacyModel = await window.spacy.load("en_core_web_sm");
let doc = await spacyModel(text);
let entities = doc.ents;
entitiesDiv.innerHTML = "<h3>Entities:</h3>";
entities.forEach(entity => {
entitiesDiv.innerHTML += `<p>${entity.text} (${entity.label_})</p>`;
});
}
async function extractRelations(text) {
let nlp = await window.nlp.load();
let doc = await nlp(text);
let relations = await doc.match("[$person] is [a] [$job title]").out("array");
relationsDiv.innerHTML = "<h3>Relations:</h3>";
relations.forEach(relation => {
relationsDiv.innerHTML += `<p>${relation}</p>`;
});
}
let submitButton = document.getElementById("submit-button");
submitButton.addEventListener("click", async () => {
let inputText = document.getElementById("input-text").value;
await recognizeEntities(inputText);
await extractRelations(inputText);
});
</script>
</body>
</html>
```
这段代码使用了NLTK和SpaCy来识别输入文本中的实体和关系。当用户点击“提交”按钮时,代码会调用`recognizeEntities`函数和`extractRelations`函数来提取实体和关系。结果会显示在页面上的`entitiesDiv`和`relationsDiv`元素中。
需要注意的是,这个代码示例中使用的是SpaCy的英文模型,如果需要处理其他语言的文本,需要下载相应的模型。此外,使用这个代码示例需要在本地运行一个Web服务器,因为浏览器中的JavaScript代码无法直接访问本地文件系统。可以使用Python内置的`http.server`模块来启动一个简单的Web服务器。例如,在命令行中输入以下命令即可启动一个Web服务器:
```
python -m http.server
```
然后,在浏览器中打开`http://localhost:8000`,即可看到运行的结果。
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