Natural language processing is an important direction in the field of computer science and artificial intelligence. It studies various theories and methods that enable effective communication between humans and computers in natural language. Natural language processing is a science that integrates linguistics, computer science, and mathematics. Therefore, research in this field will involve natural language, the language that people use every day, so it is closely related to the study of linguistics, but there are important differences. Natural language processing is not a general study of natural language, but rather a computer system that can effectively implement natural language communication, especially a software system therein. It is therefore part of computer science. Natural Language Processing (NLP) is a field of computer science, artificial intelligence, and linguistics that focus on the interaction between computers and human (natural) languages. Summarizing the tortuous history of the development of natural language processing, we can see that rule-based rationalism and statistical-based empirical methods have their own merits. Therefore, we should use scientific attitudes to analyze their advantages and disadvantages. * The rules in the rule-based rationalism method are mainly linguistic rules. These rules have strong formal description ability and form generation ability, and have good application value in natural language processing. * Rule-based rationalism can effectively deal with difficult problems such as long-distance dependencies in syntactic analysis, such as subject-verb agreement between long-distance subjects and predicate verbs in sentences. Problem, wh shift (wh-movement) problem. * Rule-based rationalism is usually easy to understand, clear and well-defined, and many linguistic facts can be directly and clearly expressed using the structure and composition of the language model. * Rule-based rationalism is inherently non-directional. Language models developed using such methods can be applied to both analysis and generation, so that the same language model can be used in both directions. * Rule-based rationalism can be used on all planes of language knowledge, and multidimensional applications can be applied in different dimensions of language. This method can be used not only in the study of speech and morphology, but also in the analysis of syntax, semantics, pragmatics, and text. * Rule-based rationalism is compatible with some efficient algorithms proposed in computer science. For example, the use of Earley algorithm (proposed in 1970) and Marcus algorithm (proposed in 1978) in computer algorithm analysis can be used as rule-based rationalism. The method is effectively used in natural language processing. * The language model developed by the rule-based rationalism method is generally fragile and has poor robustness. Some non-essential errors that deviate slightly from the language model often make the entire language model not work properly or even cause serious problems. s consequence. However, some robust and flexible profiling techniques have recently been developed that enable rule-based profiling systems to recover from profiling failures. * When using a rule-based rationalism approach to develop a natural language processing system, it often requires the cooperation of linguists, phoneticians, and various experts to conduct knowledge-intensive research, and the intensity of research work is large; rule-based Language models cannot be automatically obtained through machine learning methods, nor can they be automatically generalized using a computer. * Natural language processing systems designed using rule-based rationalism are more targeted and difficult to upgrade. For example, Slocum pointed out in 1981 that the LIFER natural language knowledge processing system has become so complicated and large after two years of research and development that the original design of this system is difficult to Make a few changes. A slight change to this system will cause the entire continuous "ripple effect", so that "the whole body is moved", and such side effects are unavoidable and eliminated. * Rule-based rationalism tends to perform as well as statistically based empirical methods in practical use. Because statistical-based empirical methods can be continually optimized based on actual training data, rule-based rationalism is difficult to adjust based on actual data. Rule-based methods are difficult to model local constraint relationships in languages. For example, word-first relationships are very useful for word class labeling, but rule-based rationalism is difficult to simulate. However, although the rule-based rationalism approach has one or the other deficiency, this method is ultimately the most in-depth research in natural language processing. It is still a very valuable and very powerful technology, and we must not ignore it. This method. It turns out that the algorithm based on the rule-based rationalism method is universal and will not lose its effect due to different languages. These algorithms are not only applicable to Western languages ​​such as English, French, German, but also to Chinese, Japanese, Korean, etc. Oriental language. In some highly targeted applications, rule-based rationalism is essential in systems that require rich linguistic knowledge support, especially in natural language processing systems that need to deal with long-distance dependencies. of. * Using statistical-based empirical methods to train linguistic data, and automatically or semi-automatically acquire statistical knowledge of the language from trained linguistic data, can effectively establish a statistical model of the language. This method works well in the automatic processing of text and speech, and is also beginning to show off in syntactic automatic analysis and word sense disambiguation. * The effect of a statistically based empirical approach relies to a large extent on the scale of the training language data. The more language data is trained, the better the statistically empirical approach works. In statistical machine translation, the size of the corpus, especially the size of the target language corpus used to train the language model, plays a pivotal role in improving system performance. Therefore, the performance of natural language processing systems can be continuously improved by expanding the size of the corpus. * Statistically based empirical methods are easily combined with rule-based rationalism methods to deal with a variety of constraints in the language, so that the effects of natural language processing systems are continually improved. * Statistically based empirical methods are well suited to simulate nuanced, inaccurate, vague concepts (such as "rare, many, several", etc.) that require ambiguity in traditional linguistics. The fuzzy logic can be processed. * A natural language processing system developed using a statistically based empirical approach whose run time grows linearly proportional to the number of symbol categories included in the statistical model, whether in the classification of the training model or in the classification of the test model. This is the case. Therefore, if the number of symbol classes in the statistical mode increases, the operating efficiency of the system will be significantly reduced. * Under the current corpus technology, using statistical-based empirical methods to obtain training data for a particular application area is a time-consuming and laborious task, and it is difficult to avoid mistakes. The effect of statistically based empirical methods is closely related to the size, representativeness, correctness and processing depth of the corpus. It can be said that the quality of the corpus used to train the data determines the statistical-based experience to a large extent. The effect of the tactical approach. * The statistically-based empirical approach is prone to data sparseness. As the size of the training corpus increases, the problem of sparse data becomes more and more serious. This problem needs to be solved by various smoothing techniques.
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