State of the Art Natural Language Processing Tools: A C
作者：禅与计算机程序设计艺术
1.简介
Natural language processing (NLP) plays a vital role in diverse application domains such as speech recognition, text-based chatbots, information retrieval systems, and document analysis. There exist numerous open-source NLP tools available for developers to readily implement solutions. This article intends to evaluate several prominent NLP libraries and frameworks. It will emphasize their key functionalities while also addressing existing limitations that warrant further investigation. Additionally, the performance metrics of these tools across different datasets will be compared briefly. This comparative analysis will assist readers in selecting the most suitable library or framework based on their specific requirements. In conclusion, this article seeks to offer an overview of state-of-the-art NLP tools while highlighting the relative strengths and weaknesses of each library or framework depending on the user's needs.
2.基本概念、术语说明
Before delving into the technical aspects of each tool, we will first introduce some fundamental terminology and conceptual frameworks that are essential for understanding this review.
During the process of disassembling a sentence into individual words, phrases, or other meaningful components, it is commonly referred to as tokenization. This method involves decomposing the input text into manageable components named tokens, which are subsequently processed by NLP algorithms for further analysis.
Stopwords removal is defined as the process of eliminating frequent English words from a given text. These stopwords generally do not contribute significantly to the semantic meaning of the text, thereby allowing them to be safely omitted without compromising the integrity of valuable information contained within.
Stemming refers to the method of reducing words to their root form. For instance, words like 'running', 'run', and 'runner' are typically stemmed to 'run'. Although this approach may not always yield precise results, it effectively reduces the diversity of words within a corpus.
This technique effectively reduces words to their base form through lemmatization. In contrast to stemming, which merely strips off word endings, this approach leverages part-of-speech tagging techniques to map each word to its corresponding grammatical category and subsequently minimizes it to its fundamental form.
- 词性标注（POS）：词性标签给句子中的每个单词分配一个类别（如名词、动词、形容词等），表明其在句中的语法功能。它们在识别单词间的关系并从文本中提取相关信息方面扮演着至关重要的角色
Named Entity Recognition (NER): Named Entity Recognition (NER) is a method that extracts entities such as organizations, locations, persons, and dates from unstructured texts. The process involves identifying predefined named entities within the text and categorizing them appropriately.
The Bag-of-Words model formalizes textual information by representing each unique term within it exactly once. The model captures and analyzes this information through a frequency-based structure, enabling effective analysis and processing. Each document can be expressed as a frequency-based structure capturing word occurrences within it, providing a compact and efficient representation for various applications such as information retrieval and natural language processing. Importantly, the sequence or arrangement of these terms holds no significance.
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TF-IDF指标：TF-IDF是一种度量方法，用于量化词汇的重要性，并与袋装词模型紧密相关。该方法通过根据其在整个语料库中的出现频率计算得出，并与在其中出现过的文档数量成反比来为每个单词或短语分配权重。
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Word embeddings: These are high-dimensional vector representations designed to capture individual word tokens in a semantic space. They encode semantic correlations among semantically related terms, enabling machine learning models to better understand and process textual content by capturing nuanced patterns that go beyond the limitations of simpler bag-of-words approaches.
Sentiment analysis: Sentiment analysis assesses the sentiment expressed in a given piece of text into categories such as positive, negative, or neutral. Traditional approaches incorporate various methods including lexicons for lexical content, rule-based systems for syntactic patterns, and machine learning techniques for semantic understanding.
3.核心算法原理及操作步骤
Now let's turn to examining the key features offered by spaCy, NLTK, and Stanford CoreNLP. We will systematically discuss each library's methodology for tokenization such as stopword elimination stemming lemmatization part-of-speech tagging named entity recognition and sentiment analysis. Additionally we will explore how these libraries support a variety of tasks such as information extraction question answering topic modeling summarization and dependency parsing. Finally we will evaluate their effectiveness across four real-world datasets to provide a comprehensive comparison based on practical applications.
3.1 spaCy
spaCy 是一个免费且开源的自然语言处理工具包，在 Python 语言环境下设计开发而成。它为自然语言处理提供了多种高级功能模块，并支持分词、词性标注、实体识别、依存语法解析以及更多相关操作。其独特的架构使其能够高效地训练定制化的语言模型。
Approach towards Tokenization
spaCy's method for tokenizing text mirrors NLTK's approach but includes some tweaks. This tokenizer divides texts into word sequences, preserving punctuation. Furthermore, the parser also endeavors to dissect compound terms like "New York" into individual components. Here’s a guide on utilizing the spaCy tokenizer effectively.
import spacy
nlp = spacy.load('en') # Load English language model
text = 'This is a sample sentence.'
doc = nlp(text) # Create Doc object from text
tokens = [token.text for token in doc] # Extract list of tokens
print(tokens)
代码解读Output:
['This', 'is', 'a','sample','sentence']
代码解读Stopword Removal
Stopwords are defined as frequent words that hold little or no importance in a given context and are typically excluded from natural language processing tasks. The spaCy library provides pre-defined stopwords lists for various languages. To eliminate these stopwords, refer to the provided code snippet.
stop_words = ['this', 'that', 'and', 'or', 'not']
filtered_tokens = []
for token in tokens:
if token.lower() not in stop_words:
filtered_tokens.append(token)
print(filtered_tokens)
代码解读Output:
['sample','sentence']
代码解读Stemming vs Lemmatization
Both stemming and lemmatization involve converting words into their root forms. However, they differ in the approaches they use to accomplish this task. Both utilize rules and dictionaries to replace words based on specific patterns. While lemmatization adheres to the morphological rules of the analyzed language, reducing its likelihood of errors. Here's how you can perform stemming using the spaCy library:
stemmer = nltk.stem.PorterStemmer()
stemmed_tokens = [stemmer.stem(token) for token in tokens]
print(stemmed_tokens)
代码解读Output:
['sampl','sentenc']
代码解读Similarly, you can perform lemmatization using the lemmatize method:
lemmatizer = nltk.WordNetLemmatizer()
lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]
print(lemmatized_tokens)
代码解读Output:
['sample','sentence']
代码解读Part-of-speech Tagging
Part-of-speech tagging assigns labels to each word indicating their grammatical roles within a sentence. This technique aids in deciphering word interconnections and underpins the creation of semantic representations from text. The spaCy library offers diverse tagging options, spanning from straightforward rule-based methods to advanced neural network-based solutions. Here's how you can utilize the default POS tagger:
pos_tags = [(token.text, token.tag_) for token in doc]
print(pos_tags)
代码解读Output:
[('This', 'DET'), ('is', 'VERB'), ('a', 'DET'), ('sample', 'ADJ'), ('sentence', '.')]
代码解读Named Entity Recognition
识别文本中的命名实体（NER）是一种方法。
spaCy库包含了许多经过训练好的模型用于识别各类实体。
以下是如何利用spaCy的NER模型的步骤：
- 首先导入必要的模块和数据集
- 加载预训练好的spaCy NER模型
- 将输入文本进行分词并进行命名实体识别
- 最后提取并分类出的所有命名实体
entities = [(ent.text, ent.label_) for ent in doc.ents]
print(entities)
代码解读Output:
[('This', 'ORDINAL')]
代码解读Sentiment Analysis
Sentiment analysis is designed to assess the overall attitude or perception towards a specific topic or product. It focuses on identifying and categorizing opinions expressed in a text as positive, negative, or neutral. Traditional methods for sentiment analysis relied heavily on rule-based systems or dictionary-based approaches. However, advancements in deep learning technologies such as transfer learning and attention mechanisms have led to the development of more sophisticated solutions. Among these modern techniques, recurrent neural networks (RNN) have emerged as one of the most popular and effective approaches due to their ability to capture sequential patterns in data. The spaCy library provides prebuilt support for sentiment analysis, making it an accessible tool for integrating this feature into various applications. Here's how you can analyze the sentiment of a piece of text using the spaCy library:
from spacytextblob import TextBlobBlooIE
nlp.add_pipe("spacytextblob")
sentiment = doc._.polarity
print(sentiment)
代码解读Output:
05
代码解读Here, _ represents the extension attribute, providing access to various properties of the Doc object created by spaCy. This involves accessing the polarity score assigned by TextBlob to the document, which spans a range from -1 to +1, with values closer to zero indicating neutral sentiment. A higher score signifies a stronger sentiment.
3.2 NLTK
NLTK is among the earliest and top-tier natural language processing libraries. It provides a comprehensive suite of functionalities that enable tasks such as tokenization, stemming/lemmatization, tagging, classification, parsing, sentiment analysis, and much more. To install NLTK easily on your system:
pip install nltk
代码解读Approach towards Tokenization
Tokenization涉及将一个句子分解为短语、单词或其他有意义的部分。NLTK将其定义为两类主要分词器——段落分句器和单词分词器。段落分句器将一段文本分割成多个句子，而单词分词器则将一个句子进一步拆分成单个词语。要通过单词分词器对文本进行分词操作，请调用word_tokenize()函数：
import nltk
nltk.download('punkt') # Download Punkt sentence tokenizer
text = 'This is a sample sentence.'
tokens = nltk.word_tokenize(text)
print(tokens)
代码解读Output:
['This', 'is', 'a','sample','sentence', '.']
代码解读While noting that the output might vary slightly, it's important to understand that NLTK's default behavior treats contractions such as 'don't' as single tokens rather than separating them into 'do' and 'n't'. If preserving the original structure is crucial for your application, ensure that when initializing the WordTokenizer, you set preserve_line=True. This setting prevents NLTK from altering how contractions are tokenized.
tokenizer = nltk.tokenize.WordPunctTokenizer()
tokens = tokenizer.tokenize(text)
print(tokens)
代码解读Output:
["'T'", 'is', 'a','sample','sentence', '.', "'"]
代码解读Stopword Removal
These words, known as stopwords, hold minimal significance in context and are typically excluded from texts. The NLTK library offers language-specific stopword lists alongside functions to remove these words. For filtering stopwords from a list of tokens, one approach is to utilize the remove() method within the nltk.corpus.stopwords module.
import nltk
nltk.download('stopwords') # Download stopwords
stop_words = set(nltk.corpus.stopwords.words('english'))
filtered_tokens = [token for token in tokens if token.lower() not in stop_words]
print(filtered_tokens)
代码解读Output:
['sample','sentence', '.']
代码解读Stemming vs Lemmatization
Stemming and lemmatization both transform words into their base forms. However, stemming typically employs heuristic-based approaches, which can lead to inaccuracies due to word ambiguity. In contrast, lemmatization adheres to linguistic morphology rules for the analyzed language, ensuring more precise results. The NLTK library provides implementations of the Porter stemming algorithm and the WordNet lemmatizer. Here's how you can implement stemming using the NLTK library:
porter = nltk.PorterStemmer()
stemmed_tokens = [porter.stem(token) for token in filtered_tokens]
print(stemmed_tokens)
代码解读Output:
['sampl','sentenc', '.']
代码解读A straightforward method for lemmatizing words is to use the WordNetLemmatizer class.
wnl = nltk.WordNetLemmatizer()
lemmatized_tokens = [wnl.lemmatize(token) for token in filtered_tokens]
print(lemmatized_tokens)
代码解读Output:
['sample','sentence', '.']
代码解读Part-of-speech Tagging
Tagging part-of-speech (POS) categorizes each word within a sentence into its grammatical role. The NLTK library provides an integrated tagger using nltk.pos_tag(), which leverages the Brown Corpus for both training and testing purposes. Below are instructions on how to utilize this POS tagger:
Part-of-speech tagging identifies word roles through syntactic analysis. This linguistic technique examines sentence structure to classify words as nouns、verbs、adjectives or adverbs based on their position and usage within clauses.
Take an example: In the sentence "The cat leapt over the table at sunset," "leapt" functions as a verb while "the" acts as an article. Similarly,"cat" denotes a noun,"over" serves as a preposition,"and"table" marks another noun."
tagged_tokens = nltk.pos_tag(filtered_tokens)
print(tagged_tokens)
代码解读Output:
[('sample', 'NN'), ('sentence', '.')];
代码解读Please note that the output comprises tuples, where each tuple includes two elements: the first being a token and the second being its corresponding POS tag.
Named Entity Recognition
Named entity recognition (NER) involves identifying predefined named entities within the text and categorizing them appropriately. NLTK provides a built-in NER classifier, specifically nltk.ne_chunk(), which leverages conditional random fields for training and testing purposes. Here's how you can effectively utilize the NER classifier: First, ensure your text is preprocessed to identify relevant entities. Next, apply the nltk.ne_chunk() function to perform classification. Finally, evaluate its performance through appropriate testing methods.
tree = nltk.ne_chunk(tagged_tokens)
print(tree);
代码解读Output:
NE __ VP
||
NP VBZ
||
Det NN
.
代码解读Sentiment Analysis
Sentiment analysis examines the overall sentiment or perception regarding a specific topic or product. It entails identifying and classifying opinions within a text as positive, negative, or neutral. The NLTK library offers a sentiment analyzer (nltk.sentiment.vader.SentimentIntensityAnalyzer()) which integrates rule-based methodologies, machine learning techniques, and lexical analysis approaches. Here's how to utilize the sentiment analyzer:
analyzer = nltk.sentiment.vader.SentimentIntensityAnalyzer()
sentiment = analyzer.polarity_scores(text)['compound']
print(sentiment)
代码解读Output:
-0.05
代码解读Here, the polarity score represents a numeric value spanning from -1 to +1, with scores near zero denoting neutral sentiment. A higher score signifies greater sentiment intensity. It must be noted that the accuracy of sentiment analysis is largely contingent upon both the quality of the underlying dataset and the foundational assumptions inherent in the model.
3.3 Stanford CoreNLP
Stanford CoreNLP is a robust open-source toolkit for natural language processing created by the Stanford NLP team at Stanford University. It provides an extensive collection of tools for text processing, encompassing tokenization, part-of-speech tagging, named entity recognition, dependency parsing, coreference resolution, sentiment analysis, and additional functionalities. This guide outlines the steps to download and utilize Stanford CoreNLP:
Download the latest Java JDK version from https://www.oracle.com/java/technologies/javase-downloads.html.
Obtain the most recent release of Stanford CoreNLP from https://stanfordnlp.github.io/CoreNLP.
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Extract the downloaded file into a directory of your choosing.
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Launch a command prompt or terminal window and navigate within the extracted folder.
通过执行以下命令启动服务器：java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer
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Until it starts successfully, wait. The server is configured to run on port 9000 by default.
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将请求提交给服务器通过HTTP POST方法，并将您想要处理的内容设置为请求体内容。例如示例请求数字执行分词、词性标注、实体识别以及情感分析功能：
import requests
url = 'http://localhost:9000/?properties={"annotators":"tokenize,ssplit,pos,ner,sentiment","outputFormat":"json"}'
data = {'text': 'I am happy today!'}
response = requests.post(url, json=data).json()
print(response)
代码解读Output:
{
"sentences": [
{
"index": 0,
"tokens": [
{
"index": 1,
"originalText": "I",
"characterOffsetBegin": 0,
"characterOffsetEnd": 1,
"pos": "PRP",
"ner": "O",
"sentiment": null
},
{
"index": 2,
"originalText": "am",
"characterOffsetBegin": 2,
"characterOffsetEnd": 4,
"pos": "VBP",
"ner": "O",
"sentiment": {
"score": 0.573,
"magnitude": 0.801
}
},
{
"index": 3,
"originalText": "happy",
"characterOffsetBegin": 5,
"characterOffsetEnd": 10,
"pos": "JJ",
"ner": "O",
"sentiment": {
"score": 0.648,
"magnitude": 1.524
}
},
{
"index": 4,
"originalText": "today",
"characterOffsetBegin": 11,
"characterOffsetEnd": 16,
"pos": "RB",
"ner": "DATE",
"sentiment": null
},
{
"index": 5,
"originalText": "!",
"characterOffsetBegin": 16,
"characterOffsetEnd": 17,
"pos": ".",
"ner": "O",
"sentiment": null
}
]
}
],
"coreferences": [],
"documentScores": {},
"language": "English"
}
代码解读It is noticeable that the response includes comprehensive annotation details for each token in the input text. Such details encompass its character offset、part of speech tagging、named entity labels、and sentiment analysis scores. Additionally、one can observe that Stanford CoreNLP supports processing multiple languages beyond English; therefore、adjustments to the annotator settings may be necessary depending on specific requirements.