Field Association Research Articles

“Keeping Condition”: Gender Ambiguity and Sexual Citizenship of Queer Male Thai Classical Musicians

AbstractGender pluralism has become a rich scholarly topic in Southeast Asia, especially with the rise of LGBT mobilisations across the region in the past decade. Transgender ritual specialists dominate the field of study, with a growing body of literature on Southeast Asian queer expressive cultures. However, queer performances in several classical performing arts are often excluded from the scholarship due to the field's association with the “traditional”. In this article, I address this scholarly gap by examining the social lives of queer men in Thai classical string music. I focus on a strategy called kep aakaan or “keeping condition” in which the musicians in question carefully articulate and disarticulate their femininity to render their gender nonconformity ambiguous under the heteronormative gaze. Using ethnographic methods to investigate the lived experience of queer male musicians on and off the stage, I show that “keeping condition” is not just about managing the display of queer potential but is also closely intertwined with morality and citizenship. Drawing on the ideas of tacit subjects and contextual sensitivities, I argue that “keeping condition” exposes the fluidity in which queer musicking bodies move between queerness and heteronormativity in ways that are more reconciliatory than confrontational. By focusing on the complicities of queerness and heteronormativity, I argue that non-normative classical performing arts can be a productive site for critical gender, sexuality, and queer studies in Southeast Asia, cutting through the “traditional” and “modern” divide that looms large in the region.

Improvements of Automatic Extraction of FA Words Tendency using Non_linear Approach

Field association (FA) terms are used to identify the subject of text (document field) by extracting specific words in that text. In this paper we use FA terms to study the effect of time change on specific terms by calculating the frequency of this terms, which associated with the archive field in a specific period. This paper also introduces a new approach for automatic evaluation of the stabilization classes using non-linear approach. The stabilization classes refer to the changing of FA terms with time in a specific period. The new approach improves the performance of decision tree than linear approach by using non-linear approach. The corpus that used in this approach has number of 1,356 files, and it is about 7.49 MB, after comparing the presented approach with the traditional one, we conclusion that the new approach enhanced the F-measure for increment, steady, decrement classes by 7.7%, 3.1%, 2.2%, sequentially.  

Text Rank for Domain Specific Using Field Association Words

Text Rank is a popular tool for obtaining words or phrases that are important for many Natural Language Processing (NLP) tasks. This paper presents a practical approach for Text Rank domain specific using Field Association (FA) words. We present the keyphrase separation technique not for a single document, although for a particular domain. The former builds a specific domain field. The second collects a list of ideal FA terms and compounds FA terms from the specific domain that are considered to be contender keyword phrases. Therefore, we combine two-word node weights and field tree relationships into a new approach to generate keyphrases from a particular domain. Studies using the changed approach to extract key phrases demonstrate that the latest techniques including FA terms are stronger than the others that use normal words and its precise words reach 90%.

A new retrieval method based on time series variation using field association terms

This paper studies the effects of the time change on the frequencies of specific terms connected to the document field in a given period. These specific terms are the field association (FA) terms. The paper also suggests a new method for automatic evaluation of the stabilization classes of FA terms to improve the precision of decision tree. The stabilization classes point out the popularity of list of FA terms depending on time change. Moreover, the suggested method manipulates the problem of the scattering of data numbers among classes to improve the performance of decision tree precision. The presented method is evaluated through conducting experiments by simulating the result of 1245 files, which are equivalent to 4.15 MB. The F‐measure for increment, fairly steady, and decrement classes achieves %90.4, %99.3, and %38.6, sequentially.

Extraction, selection and ranking of Field Association (FA) Terms from domain-specific corpora for building a comprehensive FA terms dictionary

Field Association (FA) Terms—words or phrases that serve to identify document fields are effective in document classification, similar file retrieval and passage retrieval. But the problem lies in the lack of an effective method to extract and select relevant FA Terms to build a comprehensive dictionary of FA Terms. This paper presents a new method to extract, select and rank FA Terms from domain-specific corpora using part-of-speech (POS) pattern rules, corpora comparison and modified tf-idf weighting. Experimental evaluation on 21 fields using 306 MB of domain-specific corpora obtained from English Wikipedia dumps selected up to 2,517 FA Terms (single and compound) per field at precision and recall of 74–97 and 65–98. This is better than the traditional methods. The FA Terms dictionary constructed using this method achieved an average accuracy of 97.6% in identifying the fields of 10,077 test documents collected from Wikipedia, Reuters RCV1 corpus and 20 Newsgroup data set.

Building of field association terms based on links

Studying co-word relations between field association (FA) terms and candidate terms can provide valuable information which helps the machine to take the correct decision and append the candidate terms in their suitable place inside FA terms dictionary. In this paper, a pure automatic tool to build a dynamic FA terms dictionary is described. The concept of link power as well as modifications of the rules used to determine the FA terms level depending on the concept of links will be presented. The results showed that the precession and recall were improved. In our simulation, the average improvement was about 16%. Also the complete dynamic system was built and tested.

Improvement of building field association term dictionary using passage retrieval

Field Association ( FA) terms are a limited set of discriminating terms that can specify document fields. Document fields can be decided efficiently if there are many relevant FA terms in that documents. An earlier approach built FA terms dictionary using a WWW search engine, but there were irrelevant selected FA terms in that dictionary because that approach extracted FA terms from the whole documents. This paper proposes a new approach for extracting FA terms using passage (portions of a document text) technique rather than extracting them from the whole documents. This approach extracts FA terms more accurately than the earlier approach. The proposed approach is evaluated for 38,372 articles from the large tagged corpus. According to experimental results, it turns out that by using the new approach about 24% more relevant FA terms are appending to the earlier FA term dictionary and around 32% irrelevant FA terms are deleted. Moreover, precision and recall are achieved 98% and 94% respectively using the new approach.

An automatic filtering method for field association words by deleting unnecessary words

Document classification and summarization are very important for document text retrieval. Generally, humans can recognize fields such as ⟨Sports⟩ or ⟨Politics⟩ based on specific words called Field Association (FA) words in those document fields. The traditional method causes misleading redundant words (unnecessary words) to be registered because the quality of the resulting FA words depends on learning data pre-classified by hand. Therefore recall and precision of document classification are degraded if the classified fields classified by hand are ambiguous. We propose two criteria: deleting unnecessary words with low frequencies, and deleting unnecessary words using category information. Moreover, using the proposed criteria unnecessary words can be deleted from the FA words dictionary created by the traditional method. Experimental results showed that 25% of 38 372 FA word candidates were identified as unnecessary and deleted automatically when the presented method was used. Furthermore, precision and F-measure were improved by 26% and 15%, respectively, compared with the traditional method.

Automatic building of new Field Association word candidates using search engine

With increasing popularity of the Internet and tremendous amount of on-line text, automatic document classification is important for organizing huge amounts of data. Readers can know the subject of many document fields by reading only some specific Field Association ( FA) words. Document fields can be decided efficiently if there are many FA words and if the frequency rate is high. This paper proposes a method for automatically building new FA words. A WWW search engine is used to extract FA word candidates from document corpora. New FA word candidates in each field are automatically compared with previously determined FA words. Then new FA words are appended to an FA word dictionary. From the experiential results, our new system can automatically appended around 44% of new FA words to the existence FA word dictionary. Moreover, the concentration ratio 0.9 is also effective for extracting relevant FA words that needed for the system design to build FA words automatically.

Documents similarity measurement using field association terms

Conventional approaches to text analysis and information retrieval which measured document similarity by using considering all of the information in texts are a relatively inefficiency for processing large text collections in heterogeneous subject areas. This paper outlined a new text manipulation system FA-Sim that is useful for retrieving information in large heterogeneous texts and for recognizing content similarity in text excerpts. FA-Sim is based on flexible text matching procedures carried out in various contexts and various field ranks. FA-Sim measures texts similarity by using specific field association (FA) terms instead of by comparing all text information. Similarity between texts is faster and higher by using FA-Sim than other two analysis methods. Therefore, Recall and Precision significantly improved by 39% and 37% over these two traditional methods.

A new method for selecting English field association terms of compound words and its knowledge representation

This paper presents a strategy for building a morphological machine dictionary of English that infers meaning of derivations by considering morphological affixes and their semantic classification. Derivations are grouped into a frame that is accessible to semantic stem and knowledge base. This paper also proposes an efficient method for selecting compound Field Association (FA) terms from a large pool of single FA terms for some specialized fields. For single FA terms, five levels of association are defined and two ranks are defined, based on stability and inheritance. About 85% of redundant compound FA terms can be removed effectively by using levels and ranks proposed in this paper. Recall averages of 60–80% are achieved, depending on the type of text. The proposed methods are applied to 22,000 relationships between verbs and nouns extracted from the large tagged corpus.

A document classification method by using field association words

Although there is much research of text classification based on vector spaces using word information in the whole text, generally humans can recognize the field by finding the specific words. In this paper, such words are called a field association (FA) word that can be directly related to the field classification. This paper presents an early field understanding method using FA words. Five criteria of FA words are defined for hierarchical fields and a method of extracting shorter FA words of compound words is proposed. The presented approach is estimated by the simulation results of 140 fields' text files.

Epidemiological research on ageing: an orientation

Epidemiology of ageing is a field distinct from that of gerontology. It is concerned with the occurrence of different rates of the fundamental biological process of ageing, relating the rates to their determinants. In principle, it holds very exciting prospects for the preservation of youthfulness and the prevention of illness. However, its development has hardly begun, and suitable measures of the stage of ageing in an individual are not even available. The feasibility of developing such measures is being questioned today, but the grounds of this are specious. Progress is also hindered by the field's association with geriatrics today. Reorientation and reorganization are needed to help the epidemiology of ageing to get underway and to bear fruit.

Public Administration and the Critique of Domination

Public administration is viewed here as facing a crisis of legitimacy which derives from (1) the field's association with societal tendencies toward regulation and control and (2) the failure ofpublic administration theory to adequately connect with adminis trative practice. In contrast, a critical approach to public administration is outlined, one which holds forth some promise of meaningful reflection and enlightened action in the public sector.