Natural Language Processing: State of The Art, Current Trends and Challenges (1708.05148v1)

Abstract: Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. The paper distinguishes four phases by discussing different levels of NLP and components of Natural Language Generation (NLG) followed by presenting the history and evolution of NLP, state of the art presenting the various applications of NLP and current trends and challenges.

• The paper traces NLP's evolution from early rule-based systems to modern neural models, emphasizing historical milestones and paradigm shifts.
• It categorizes NLP into distinct levels such as phonological, syntactic, and semantic, clarifying the layered complexity of language understanding and generation.
• The paper highlights practical applications like machine translation and sentiment analysis while addressing challenges like ambiguity resolution and method selection.

Overview of the State of Natural Language Processing

The paper provides a comprehensive examination of NLP, detailing its history, current state, and the challenges facing the field. It systematically categorizes the phases and components of NLP, with a particular focus on Natural Language Understanding (NLU) and Natural Language Generation (NLG).

Key Contributions and Findings

1. Historical Context and Evolution:
   • The paper traces the development of NLP, originating in the late 1940s with machine translation efforts, and highlights the significant milestones in computational grammar and AI-driven linguistic processing. Influential projects like the BASEBALL Q-A system and LUNAR are noted for their contributions to advancing natural language interfaces.
2. Levels of NLP:
   • NLP is dissected into phonological, morphological, lexical, syntactic, semantic, discourse, and pragmatic levels, each contributing to the understanding and generation of human language. The explanation of these levels is critical for comprehending the complexity involved in NLP tasks.
3. NLP Applications:
   • The discussion extends to the practical applications, including machine translation, email spam detection, information extraction, and text summarization. Specific mention is made of machine translation’s reliance on statistical engines and the ongoing development of neural methods, reflecting real-world advancements and their challenges.
4. Current Tools and Systems:
   • The state-of-the-art tools such as POS taggers, chunkers, Named Entity Recognition, and sentiment analyzers are highlighted. These tools facilitate various linguistic tasks from syntactic parsing to emotion detection in multilingual contexts.
5. Challenges and Ambiguity:
   • Ambiguity resolution remains a significant challenge, with solutions like ambiguity minimization and interactive disambiguation proposed. The handling of syntactic and lexical ambiguities is crucial for improving NLP systems' accuracy.
6. Methodological Approaches:
   • The analysis encompasses diverse methodologies, including symbolic approaches, statistical models, and machine learning paradigms like Hidden Markov Models (HMMs) and the Naive Bayes classifiers. The distinction between generative and discriminative models is pivotal for selecting appropriate methods in specific NLP applications.

Implications and Future Directions

The paper underscores the multifaceted nature of NLP, driven by both linguistic theory and computational advances. Practically, the applications in fields such as healthcare and social media demonstrate NLP's growing impact. The paper suggests potential growth areas, including unsupervised learning techniques and the integration of semantic understanding, which could lead to more sophisticated dialogue systems and automated summarization tasks.

Future developments could leverage advancements in artificial neural networks and deep learning, driving forward applications in real-time translation and context-aware language processing. Continuous refinement of statistical and symbolic techniques will be vital for overcoming current limitations and enhancing NLP's ability to process complex human language nuances.

In conclusion, the paper provides a thorough exploration of NLP, presenting a balanced view of its accomplishments and the challenges that lie ahead. The ongoing research and development in this field are set to transform computational linguistics, making human-computer interaction more natural and intuitive.