computational linguistics

computational linguistics

Advancements in Computational Linguistics: Theory and Applications

1. Introduction to Computational Linguistics

In this chapter, we describe how language is modeled as data in computational linguistics, including methods for syntactic parsing used for disambiguating meaning, supervised and unsupervised word sense induction and disambiguation, and computational neural models for word embeddings. Other important NLP applications, which cover text-to-speech synthesis, information retrieval, machine translation, and text classification, will not be detailed in this chapter. However, they are used in many real-world applications such as indexing enterprise data for fast reports, indexing news articles to provide search for large user communities, converting text from one language to another, and classifying documents according to content, user, or category, among many others.

Advancements in theoretical comprehension and practical implementation of natural language processing tasks and applications have become a prevalent topic in research communities. Advances in technology and computation hardware have allowed us to develop tools that perform tasks that were considered challenging to tackle just ten years ago. A lot of these breakthroughs in our ability to understand and work with linguistic objects come from results in the cognitive sciences and from the application of these concepts to develop scalable algorithms based on them. Consequently, computational methods are becoming more efficient, robust, and precise, and in turn, large-scale real-world language technologies are becoming increasingly pervasive in everyday life.

2. Key Theoretical Concepts in Computational Linguistics

In a broad sense, CL research is designed to mimic human capability to analyze, parse and generate language. “Opening up” the language acquisition “black box” provides many challenges, but also opportunities to better understand more general aspects of the human mind. Linguistics, recognizing the ensemble of properties that make up human language, is an appropriate scientific field. Other relevant fields include machine learning, computer science, and psychology, with an increasing movement towards cross-disciplinary research. In today’s Information Society, CL technology is as crucial as information technology (IT) when it comes to designing and developing techniques for human-computer interaction (HCI) and for constructing new intelligent non-human agents.

Computational Linguistics, or CL, is an interdisciplinary field of research that draws upon theoretical concepts and methodologies from a variety of academic disciplines. Within CL, many projects emphasize the integration of natural language processing (NLP) and corpus linguistics methods. Bringing these two areas of research together in practical applications offers compelling contemporary ways to address IT challenges in the development and management of societal resources.

3. Applications of Computational Linguistics in Natural Language Processing

The fast-growing research area of computational linguistics and natural language processing (NLP) is fueled by the hands-on creative roles which the underlying language technologies play in numerous industrial or scientific applications. Increasing part-of-speech (PoS) models’ availability and diversity offer flexibility for performing data-driven NLP research of different types of text data – whether it contains language of historical, specialized, or conversational style or can be only partially labeled or optical character recognition (OCR)-generated. In this study, we investigate the skill of traditionally trained and the continually optimized models to cope with real data constraints—specifically in author, name, and location tracking in free text queries addressed to the Europeana search service. Our cross-linguistic and cross-epoch evaluation on the Europeana query log in German, Dutch, and Italian show performance deficiencies and similarity in traditional models of different heading but distinguish themselves from work well present-time queries. This leads to a proposal for the future development of country-specific, OCR-processed retrieval follow-up in NLP research.

Computational linguistics and natural language processing, designed with the integration of natural language, artificial intelligence, and the network, constitute the driving force of computer information technology and theory. In this article, the development history of natural language processing (NLP) is revealed. The key research of NLP includes information extraction, corpus study design, speech understanding, text analysis, machine translation, knowledge acquisition, question and answer system, dialog system, language generation, etc. In recent years, with the rapid development of data mining and big data technology, more and more attention has been paid to natural language processing by scholars in various fields. Milne and Witten carried out a large study of natural language processing, mainly involving the interaction between computer science, linguistics, management science, cognitive science, etc.

4. Challenges and Future Directions in Computational Linguistics

The major challenge for computational linguistics in the near future is to find ways of obtaining fully accurate annotations. The work in the community so far has concentrated on refining the most difficult annotations. Going forward, the entire surface is open: there are many different data conditions and many more propositions to annotate them with. Our society needs progress over the full range so that natural language processing reaches the majority of language users and opens the way for applications we haven’t even dreamed of. As we know, using common sense to disambiguate meanings is easier for humans than creating a system for computers to use. This is where research activities can be invested to overcome lexico-semantic ambiguities.

In this article, we gave a brief history of computational linguistics, discussed various representation models for linguistic structures and outcomes of applying different parsing strategies, and presented applications of computational linguistics. The current state-of-the-art is successful in obtaining a wide range of robust linguistic annotations with high precision. However, future research leading up to fully accurate annotations will enable many new exciting applications, most of which require more complex and accurate linguistic annotations than are currently available.