新西兰论文网:自然语言工程

新西兰论文网:自然语言工程

这个报告包含找到的目标词汇可以描述的特定方面电影审查。电影的具体方面,被认为是:情节、人物,摄影和对话。这是找到所有这些话表达审查员的审查意见的其中一个方面的电影。我们的想法是,这将提供细粒度特征的意见被审查的作者表示。这是基于假设认为人们正在寻找在评论出现在一个句子的单词在一个特定的(依赖)关系到我们的一个方面的话(“阴谋”,“角色”,“摄影”和“对话”)。例如,意见词“神奇”可能会被发现,因为它是一个句子中使用形容词的地方修改方面“阴谋”一词,在句子“我认为这有一个惊人的阴谋”。

看来提取算法

这问题的质量Python代码实现(编码风格,适当使用注释和效率)和伪代码。为了遵循测试例子,遵循以下步骤。

取代load_parsed_dvd_sentences(方面)load_parsed_example_sentences()

确保你从sussex_nltk进口load_parsed_example_sentences。解析

伪代码:

从sussex_nltk。解析进口load_parsed_example_sentences

parsed_example_sentences = load_parsed_example_sentences()

#检查句子,你可以直接打印出来

在parsed_example_sentences parsed_sentence:

打印”——句子——“

打印parsed_sentence

现在,这些算法提供的扩展讨论如下。

新西兰论文网:自然语言工程

This report contains the goal of finding the words that could describe the particular aspects of the film being reviewed. The specific aspects of films that would be considered are: the plot, the characters, the cinematography and the dialogue. This is to find all of those words in a review that express the reviewer’s opinion about one of these aspects of the film. The idea is that this will provide a fine-grained characterization of the opinion being expressed by the author of the review. This is based on the assumption that the opinion words people are looking for are words that occur in a sentence in the review in a particular (dependency) relationship to one of our aspect words (“plot”, “characters”, “cinematography” and “dialogue”). For example, the opinion word “amazing” might be found because it is used in a sentence where it is an adjective modifying the aspect word “plot”, as in the sentence “I thought it had an amazing plot”.
Opinion Extraction Algorithm
This concerns both the quality of both the Python code implementation (coding style, appropriate use of comments and efficiency) and the pseudo code. In order to follow the test examples, just follow the below given steps.
 Replace load_parsed_dvd_sentences (aspect) with load_parsed_example_sentences (), and
 Ensure that you’re importing load_parsed_example_sentences from sussex_nltk. parse
Pseudo code:
From sussex_nltk. parse import load_parsed_example_sentences

parsed_example_sentences = load_parsed_example_sentences ()

# To inspect the sentences, you could print them straight out
For parsed_sentence in parsed_example_sentences:
Print “— Sentence —”
Print parsed_sentence
Now, the extensions provided with these algorithms are discussed below.

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