Python自然语言处理学习笔记(7)：1.5 自动理解自然语言
Updated log
1st:2011/8/5
1.5 Automatic Natural Language Understanding 自然语言的自动理解
We have been exploring language bottom-up(自底向上的), with the help of texts and the Python programming language. However, we’re also interested in exploiting(利用) our knowledge of language and computation by building useful language technologies. We’ll take the opportunity now to step back(后退) from the nitty-gritty(细节) of code in order to paint a bigger picture of natural language processing.
At a purely practical level, we all need help to navigate the universe of information locked up in text on the Web. Search engines have been crucial to the growth and popularity of the Web, but have some shortcomings(缺点). It takes skill, knowledge, and some luck, to extract answers to such questions as: What tourist sites can I visit between Philadelphia(费城) and Pittsburgh(匹兹堡) on a limited budget? What do experts say about digital SLR cameras? What predictions about the steel market were made by credible(可信的) commentators in the past week? Getting a computer to answer them automatically involves a range of language processing tasks, including information extraction, inference(推理), and summarization(概括), and would need to be carried out on a scale and with a level of robustness(健壮性) that is still beyond our current capabilities(目前仍在我们的能力之外).
On a more philosophical level, a long-standing(存在已经的) challenge within artificial intelligence has been to build intelligent machines, and a major part of intelligent behavior is understanding language(智能行为的主要部分是语言的理解). For many years this goal has been seen as too difficult. However, as NLP technologies become more mature, and robust methods for analyzing unrestricted text become more widespread, the prospect(期望) of natural language understanding has re-emerged(再次浮现) as a plausible(似乎可信的) goal.
In this section we describe some language understanding technologies, to give you a sense of the interesting challenges that are waiting for you.
Word Sense Disambiguation 词义消歧
In word sense disambiguation we want to work out which sense of a word was intended in a given context. Consider the ambiguous words serve and dish:
(2) a. serve: help with food or drink; hold an office; put ball into play
b. dish: plate; course of a meal; communications device
In a sentence containing the phrase: he served the dish, you can detect that both serve and dish are being used with their food meanings. It’s unlikely that the topic of discussion shifted from sports to crockery(陶器) in the space of three words. This would force you to invent bizarre(奇异的) images, like a tennis pro(职业网球) taking out his frustrations on(拿…出气) a china tea-set laid out(陈列) beside the court(网球场){这也许会使得你眼前产生一副怪异的画面，一个职业网球手正把他的郁闷发泄到放在网球场边上的中国茶具}. In other words, we automatically disambiguate words using context, exploiting the simple fact that nearby words have closely related meanings. As another example of this contextual(上下文的) effect, consider the word by, which has several meanings, for example, the book by Chesterton (agentive(表示动作主体的词缀(如defendant的后缀-ant))—Chesterton was the author of the book); the cup by the stove炉子(locative—the stove is where the cup is); and submit by Friday (temporal—Friday is the time of the submitting). Observe in (3) that the meaning of the italicized(斜体的) word helps us interpret the meaning of by.
(3) a. The lost children were found by the searchers (agentive) 动词主体
b. The lost children were found by the mountain (locative) 位置
c. The lost children were found by the afternoon (temporal) 时态
Pronoun Resolution 代词参较
A deeper kind of language understanding is to work out “who did what to whom,” i.e., to detect the subjects(主语) and objects(宾语) of verbs(动词). You learned to do this in elementary school(小学), but it’s harder than you might think. In the sentence the thieves stole the paintings, it is easy to tell who performed the stealing action. Consider three possible following sentences in (4), and try to determine what was sold, caught, and found (one case is ambiguous).
(4) a. The thieves stole the paintings. They were subsequently(随后) sold.
b. The thieves stole the paintings. They were subsequently caught.
c. The thieves stole the paintings. They were subsequently found.
Answering this question involves finding the antecedent(先行词) of the pronoun they, either thieves or paintings. Computational techniques for tackling this problem include anaphora resolution(指代消解)—identifying what a pronoun or noun phrase refers to—and semantic role labeling(语义角色标注)—identifying how a noun phrase relates to the verb (as agent, patient, instrument, and so on).
antecedent:
【语法学】先行词(或语、句),前在词(或语、句)(如“I saw Harry and spoke to him”中的 Harry 对 him 而言)
anaphora resolution:
【语法学】照应，指代(法)(指用代词、助动词等替换某种语法成分的方法照应或指代前面的词或词组，如I know it and he does too. 一句中的 it 和 do 的应用)
semantic role labeling:
语义角色标注(Semantic Role Labeling)指的是分析句子的论元结构，即标记出句子中某个动词的所有论元。
Generating Language Output 产生语言输出
If we can automatically solve such problems of language understanding, we will be able to move on to tasks that involve generating language output, such as question answering and machine translation. In the first case, a machine should be able to answer a user’s questions relating to collection of texts:
(5) a. Text: ... The thieves stole the paintings. They were subsequently sold. ...
b. Human: Who or what was sold?
c. Machine: The paintings.
The machine’s answer demonstrates that it has correctly worked out that they refers to paintings and not to thieves. In the second case, the machine should be able to translate the text into another language, accurately conveying(传达) the meaning of the original text. In translating the example text into French, we are forced to choose the gender(性别) of the pronoun in the second sentence: ils (masculine) if the thieves are sold, and elles (feminine) if the paintings are sold. Correct translation actually depends on correct understanding of the pronoun. 事实上，正确的翻译取决于对于代词的正确理解。
(6) a. The thieves stole the paintings. They were subsequently found.
b. Les voleurs ont volé les peintures. Ils ont été trouvés plus tard. (the thieves)
c. Les voleurs ont volé les peintures. Elles ont été trouvées plus tard. (the paintings)
In all of these examples, working out the sense of a word, the subject of a verb, and the antecedent of a pronoun are steps in establishing the meaning of a sentence, things we would expect a language understanding system to be able to do.
Machine Translation 机器翻译
For a long time now, machine translation (MT) has been the holy grail(圣杯，我想到哈利波特了…哦，那个是火焰杯) of language understanding, ultimately(根本地) seeking to provide high-quality, idiomatic(符合语言习惯的) translation between any pair of languages(翻译是按对的). Its roots go back to the early days of the Cold War, when the promise of automatic translation led to substantial government sponsorship(大量的政府赞助), and with it, the genesis of NLP itself.
Today, practical translation systems exist for particular pairs of languages, and some are integrated into web search engines. However, these systems have some serious shortcomings. We can explore them with the help of NLTK’s “babelizer” (使(习俗、语言等)产生(或陷入)混乱；使(彼此间的语言等)互不理解，使不知所云)(which is automatically loaded when you import this chapter’s materials using from nltk.book import *). This program submits a sentence for translation into a specified language, then submits the resulting sentence for translation back into English. It stops after 12 iterations, or if it receives a translation that was produced already (indicating a loop):
NLTK Babelizer: type 'help' for a list of commands.
Babel> how long before the next flight to Alice Springs?
Babel> german
Babel> run
0> how long before the next flight to Alice Springs?
1> wie lang vor dem folgenden Flug zu Alice Springs?
2> how long before the following flight to Alice jump?
3> wie lang vor dem folgenden Flug zu Alice springen Sie?
4> how long before the following flight to Alice do you jump?
5> wie lang, bevor der folgende Flug zu Alice tun, Sie springen?
6> how long, before the following flight to Alice does, do you jump?
7> wie lang bevor der folgende Flug zu Alice tut, tun Sie springen?
8> how long before the following flight to Alice does, do you jump?
9> wie lang, bevor der folgende Flug zu Alice tut, tun Sie springen?
10> how long, before the following flight does to Alice, do do you jump?
11> wie lang bevor der folgende Flug zu Alice tut, Sie tun Sprung?
12> how long before the following flight does leap to Alice, does you?
Observe that the system correctly translates Alice Springs from English to German (in the line starting 1>), but on the way back to English, this ends up as Alice jump (line 2). The preposition（介词） before is initially translated into the corresponding German preposition vor, but later into the conjunction（连词） bevor (line 5). After line 5 the sentences become non-sensical (but notice the various phrasings(分句) indicated by the commas, and the change from jump to leap). The translation system did not recognize when a word was part of a proper name, and it misinterpreted（误解） the grammatical structure. The grammatical problems are more obvious in the following example. Did John find the pig, or did the pig find John?
>>> babelize_shell()
Babel> The pig that John found looked happy
Babel> german
Babel> run
0> The pig that John found looked happy
1> Das Schwein, das John fand, schaute gl?cklich
2> The pig, which found John, looked happy
Machine translation is difficult because a given word could have several possible translations (depending on its meaning), and because word order must be changed in keeping with the grammatical structure of the target language. Today these difficulties are being faced by collecting massive quantities of parallel(类似的) texts from news and government websites that publish documents in two or more languages. Given a document in German and English, and possibly a bilingual(双语的) dictionary, we can automatically pair up(配对) the sentences, a process called text alignment(文本对齐). Once we have a million or more sentence pairs, we can detect corresponding words and phrases, and build a model that can be used for translating new text.
Spoken Dialogue Systems 对话系统
In the history of artificial intelligence, the chief measure of intelligence has been a linguistic one, namely the Turing Test（图灵测试）: can a dialogue system, responding to a user’s text input, perform so naturally that we cannot distinguish it from a human-generated response? In contrast, today’s commercial dialogue systems are very limited, but still perform useful functions in narrowly defined domains, as we see here:
S: How may I help you?
U: When is Saving Private Ryan playing?
S: For what theater?
U: The Paramount theater.
S: Saving Private Ryan is not playing at the Paramount theater, but it’s playing at the Madison theater at 3:00, 5:30, 8:00, and 10:30.
You could not ask this system to provide driving instructions or details of nearby restaurants unless the required information had already been stored and suitable question-answer pairs had been incorporated into the language processing system.(我觉得googlemap和此类似，不过的确是事先存储的信息)
Observe that this system seems to understand the user’s goals: the user asks when a movie is showing and the system correctly determines from this that the user wants to see the movie. This inference seems so obvious that you probably didn’t notice it was made, yet a natural language system needs to be endowed(赋予) with this capability in order to interact naturally. Without it, when asked, Do you know when Saving Private Ryan is playing?, a system might unhelpfully respond with a cold Yes. However, the developers of commercial dialogue systems use contextual assumptions(上下文假设) and business logic to ensure that the different ways in which a user might express requests or provide information are handled in a way that makes sense for the particular application. So, if you type When is ..., or I want to know when ..., or Can you tell me when ..., simple rules will always yield screening times. This is enough for the system to provide a useful service.
Dialogue systems give us an opportunity to mention the commonly assumed pipeline for NLP. Figure 1-5 shows the architecture of a simple dialogue system. Along the top of the diagram, moving from left to right, is a “pipeline” of some language understanding components. These map from speech input via syntactic parsing to some kind of meaning representation. Along the middle, moving from right to left, is the reverse pipeline of components for converting concepts to speech. These components make up the dynamic aspects of the system. At the bottom of the diagram are some representative bodies of static information: the repositories of language-related data that the processing components draw on to do their work.
Your Turn: For an example of a primitive dialogue system, try having a conversation with an NLTK chatbot. To see the available chatbots, run nltk.chat.chatbots(). (Remember to import nltk first.)
Figure 1-5. Simple pipeline architecture for a spoken dialogue system: Spoken input (top left) is analyzed输入分析, words are recognized单词识别, sentences are parsed句法分析 and interpreted in context上下文解释, application-specific actions take place特定用途的行为发生 (top right); a response is planned, realized as a syntactic structure, then to suitably inflected words, and finally to spoken output; different types of linguistic knowledge inform each stage of the process.
Textual Entailment 文本的含义
The challenge of language understanding has been brought into focus in recent years by a public “shared task” called Recognizing Textual Entailment (RTE). The basic scenario is simple. Suppose you want to find evidence to support the hypothesis（假设）: Sandra Goudie was defeated by Max Purnell, and that you have another short text that seems to be relevant, for example, Sandra Goudie was first elected to Parliament（议会） in the 2002 elections, narrowly winning the seat of Coromandel by defeating Labour candidate Max Purnell and pushing incumbent（现任的） Green MP Jeanette Fitzsimons into third place. Does the text provide enough evidence for you to accept the hypothesis? In this particular case, the answer will be “No.” You can draw this conclusion easily, but it is very hard to come up with automated methods for making the right decision. The RTE Challenges provide data that allow competitors(竞争对手) to develop their systems, but not enough data for “brute force” machine learning techniques (a topic we will cover in Chapter 6). Consequently, some linguistic analysis is crucial. In the previous example, it is important for the system to note that Sandra Goudie names the person being defeated in the hypothesis, not the person doing the defeating in the text. As another illustration of the difficulty of the task, consider the following text-hypothesis pair:
(7)
a. Text: David Golinkin is the editor or author of 18 books, and over 150 responsa, articles, sermons(布道) and books
b. Hypothesis: Golinkin has written 18 books
In order to determine whether the hypothesis is supported by the text, the system needs the following background knowledge:
(i) if someone is an author of a book, then he/she has written that book;
(ii) if someone is an editor of a book, then he/she has not written (all of) that book;
(iii) if someone is editor or author of 18 books, then one cannot conclude that he/she is author of 18 books.
Limitations of NLP 自然语言处理的局限性
Despite the research-led advances in tasks such as RTE, natural language systems that have been deployed for real-world applications still cannot perform common-sense(常识的) reasoning or draw on world knowledge in a general and robust manner. We can wait for these difficult artificial intelligence problems to be solved, but in the meantime(同时) it is necessary to live with some severe（严重的） limitations on the reasoning and knowledge capabilities of natural language systems. Accordingly, right from the beginning, an important goal of NLP research has been to make progress on the difficult task of building technologies that “understand language,” using superficial(表面的) yet powerful techniques instead of unrestricted knowledge and reasoning capabilities. Indeed, this is one of the goals of this book, and we hope to equip you with the knowledge and skills to build useful NLP systems, and to contribute to the long-term aspiration（抱负） of building intelligent machines.
确实，这是本书的目标之一，并且我们希望你能够获得这些知识和技能来构建有效的NLP系统，以及为构建智能机器做出长期努力的抱负。
