Are Deep Learning Approaches Suitable for Natural Language Processing?

In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understand-ing, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs)

SKOPE: A connectionist/symbolic architecture of spoken Korean processing

Spoken language processing requires speech and natural language integration. Moreover, spoken Korean calls for unique processing methodology due to its linguistic characteristics. This paper presents SKOPE, a connectionist/symbolic spoken Korean processing engine, which emphasizes that: 1) connectionist and symbolic techniques must be selectively applied according to their relative strength and weakness, and 2) the linguistic characteristics of Korean must be fully considered for phoneme recognition, speech and language integration, and morphological/syntactic processing. The design and implementation of SKOPE demonstrates how connectionist/symbolic hybrid architectures can be constructed for spoken agglutinative language processing. Also SKOPE presents many novel ideas for speech and language processing. The phoneme recognition, morphological analysis, and syntactic analysis experiments show that SKOPE is a viable approach for the spoken Korean processing.Comment: 8 pages, latex, use aaai.sty & aaai.bst, bibfile: nlpsp.bib, to be presented at IJCAI95 workshops on new approaches to learning for natural language processin

Comparative Analysis of Word Embeddings for Capturing Word Similarities

Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.Comment: Part of the 6th International Conference on Natural Language Processing (NATP 2020

A Survey of Deep Learning Approaches for Natural Language Processing Tasks

In recent years, deep learning has been a go-to method for solving difficult NLP problems. Deep learning models have attained state-of-the-art performance across a wide range of natural language processing applications, including text summarization, sentiment analysis, named entity identification, and language translation, by utilizing enormous neural network designs and massive volumes of training data. In this paper, we take a look at the most important deep learning methods and how they've been used for different natural language processing jobs. We go over the basics of neural network designs including CNNs, RNNs, and transformers, and we also go over some of the more recent developments, such as BERT and GPT-3. Our discussion of each method centers on its guiding principles, benefits, drawbacks, and significant NLP applications. To further illustrate the relative merits of various models, we also provide their comparative performance findings on industry-standard benchmark datasets. We also highlight some of the present difficulties and potential future avenues of study in deep learning applied to natural language processing. The purpose of this survey is to offer academics and practitioners in natural language processing a high-level perspective on how to make good use of deep learning in their respective fields

NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings

Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we argue that effort to developing service descriptions, request translations, and matching mechanisms could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural language-based service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 44\% while keeping a high precision/recall when matching high-level user requests with low-level service method invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on Services Computing) on July 1