The Empty Lexicon

1996 ◽  
Vol 1 (1) ◽  
pp. 99-119 ◽  
Author(s):  
John McH. Sinclair

This paper1 contrasts two views on the analysis of language. In one view, language is primarily seen as a carrier of messages in sentences whose propo-sitional content can be retrieved, and symbolised in a knowledge base. In the other, language is seen as a means of communication that deals in much more complex matters than just carrying messages. In relation to vocabulary and the design of lexicons, the model of terminology suits the first position, while in the other the lexicon is considered empty at the start and is gradually filled with the evidence of usage. Similar contrasts are made in other areas relevant to natural language processing. In one approach, the expectation is of tidiness and conformity to rules; the other stresses the inherently provisional nature of the organisation of language and, therefore, the meanings. As these two approaches encounter the vast amount of evidence stored in today's corpora, their methods and responses contrast in interesting ways.

2013 ◽  
Vol 340 ◽  
pp. 126-130 ◽  
Author(s):  
Xiao Guang Yue ◽  
Guang Zhang ◽  
Qing Guo Ren ◽  
Wen Cheng Liao ◽  
Jing Xi Chen ◽  
...  

The concepts of Chinese information processing and natural language processing (NLP) and their development tendency are summarized. There are different comprehension of Chinese information processing and natural language processing in China and the other countries. But the work appears to emerge in the study of key point of languages processing. Mining engineering is very important for our country. Though the final task of languages processing is difficult, Chinese information processing has contributed substantially to our scientific research and social economy and it will play an important part for mining engineering in our future.


2020 ◽  
pp. 034-040
Author(s):  
O.P. Zhezherun ◽  
◽  
M.S. Ryepkin ◽  
◽  

The article describes a classification system with natural language processing. Many systems use neural networks, but it needs massive amounts of data for training, which is not always available. Authors propose to use ontologies in such systems. As example of such approach it is shown the classification system, which helps to form a list of the best candidates during the recruitment process. An overview of the methods for ontologies constructing and language analyzers appropriate for classification systems are presented. The system in the form of a knowledge base is constracted. Described system supports Ukrainian and English languages. The possible ways of system expansion is regarded.


2020 ◽  
Author(s):  
Masashi Sugiyama

Recently, word embeddings have been used in many natural language processing problems successfully and how to train a robust and accurate word embedding system efficiently is a popular research area. Since many, if not all, words have more than one sense, it is necessary to learn vectors for all senses of word separately. Therefore, in this project, we have explored two multi-sense word embedding models, including Multi-Sense Skip-gram (MSSG) model and Non-parametric Multi-sense Skip Gram model (NP-MSSG). Furthermore, we propose an extension of the Multi-Sense Skip-gram model called Incremental Multi-Sense Skip-gram (IMSSG) model which could learn the vectors of all senses per word incrementally. We evaluate all the systems on word similarity task and show that IMSSG is better than the other models.


2019 ◽  
Vol 20 (K9) ◽  
pp. 23-30
Author(s):  
Le Thi Thuy ◽  
Phan Thi Tuoi ◽  
Quan Thanh Tho

Entity co-reference resolution and sentiment analysis are independent problems and popular research topics in the community of natural language processing. However, the combination of those two problems has not been getting much attention. Thus, this paper susgests to apply knowledge base to solve co- reference between object and aspect with sentiment. In addition, the paper also proposes the model of Ontology-based co-reference resolution in sentiment analysis for English text. Finally, we also discuss evaluation methods applied for our model and the results obtained.


Author(s):  
Davide Picca ◽  
Dominique Jaccard ◽  
Gérald Eberlé

In the last decades, Natural Language Processing (NLP) has obtained a high level of success. Interactions between NLP and Serious Games have started and some of them already include NLP techniques. The objectives of this paper are twofold: on the one hand, providing a simple framework to enable analysis of potential uses of NLP in Serious Games and, on the other hand, applying the NLP framework to existing Serious Games and giving an overview of the use of NLP in pedagogical Serious Games. In this paper we present 11 serious games exploiting NLP techniques. We present them systematically, according to the following structure:  first, we highlight possible uses of NLP techniques in Serious Games, second, we describe the type of NLP implemented in the each specific Serious Game and, third, we provide a link to possible purposes of use for the different actors interacting in the Serious Game.


2019 ◽  
Vol 17 (1) ◽  
pp. 89-97
Author(s):  
Qiao Li ◽  
Junming Liu

ABSTRACT Auditors' discussions in audit plan brainstorming sessions provide valuable knowledge on how audit engagement teams evaluate information, identify and assess risks, and make audit decisions. Collected expertise and experience from experienced auditors can be used as decision support for future audit plan engagements. With the help of Natural Language Processing (NLP) techniques, this paper proposes an intelligent NLP-based audit plan knowledge discovery system (APKDS) that can collect and extract important contents from audit brainstorming discussions and transfer the extracted contents into an audit knowledge base for future use.


2017 ◽  
Vol 11 (03) ◽  
pp. 345-371
Author(s):  
Avani Chandurkar ◽  
Ajay Bansal

With the inception of the World Wide Web, the amount of data present on the Internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. The research objective of this paper is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval (IR) and Natural Language processing (NLP). The focus is on using a structured and annotated knowledge base instead of an unstructured one. The knowledge base used here is DBpedia and the final system is evaluated on the Text REtrieval Conference (TREC) 2004 questions dataset.


2013 ◽  
Vol 274 ◽  
pp. 359-362
Author(s):  
Shuang Zhang ◽  
Shi Xiong Zhang

Abstract. Shallow parsing is a new strategy of language processing in the domain of natural language processing recently years. It is not focus on the obtaining of the full parsing tree but requiring of the recognition of some simple composition of some structure. It separated parsing into two subtasks: one is the recognition and analysis of chunks the other is the analysis of relationships among chunks. In this essay, some applied technology of shallow parsing is introduced and a new method of it is experimented.


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