Pragmatic annotation of a domain-restricted English-Spanish comparable corpus

This paper explores the multi-layer annotation of a written domain-restricted English-Spanish comparable corpus (CLANES – Controlled LANguage English Spanish), focusing on pragmatic annotation. The annotation scheme draws on part of speech tagging and a semantic annotation scheme, i.e. the UCREL Semantic Analysis System, with some added categories to fit the food-and-drink domain represented in CLANES. These are used to build significant (pragmatic) metapatterns. Seven different pragmatic functions have been identified in our corpus, namely <STATE>, <DIRECT>, <SUGGEST>, <RECOMMEND>, <PRAISE>, <EVIDENCE> and <RELATE TO READER>. Computer scripts translate this linguistic information into regular expressions to be used in unsupervised annotation. Partial results indicate that applying lexical restrictors boosts the success rate considerably. However, metadata is preferred because of increased replicability and generality. Replicability issues and limitations encountered during testing are also addressed.

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The development of a semantic annotation scheme for Chinese kinship

Corpora ◽

10.3366/e1749503209000306 ◽

2009 ◽

Vol 4 (2) ◽

pp. 189-208 ◽

Cited By ~ 2

Author(s):

Yufang Qian ◽

Scott Piao

Keyword(s):

Semantic Analysis ◽

Semantic Annotation ◽

Corpus Annotation ◽

Semantic Field ◽

Same Sex ◽

Annotation Scheme ◽

Marital Relations ◽

Semantic Categories ◽

Analysis System ◽

Contemporary Chinese

In this paper, we propose a corpus annotation scheme and lexicon for Chinese kinship terms. We modify existing traditional Chinese kinship schemes into a comprehensive semantic field framework that covers kinship semantic categories in contemporary Chinese. The scheme is inspired by the Lancaster USAS (UCREL Semantic Analysis System) taxonomy, which contains categories for English kinship terms. We show how our scheme works with a Chinese kinship semantic lexicon which covers parents, siblings, marital relations, off-spring and same-sex partnerships. The kinship lexicon was created through a pilot study involving the Lancaster University Mandarin Corpus. We foresee that our annotation scheme and lexicon will provide a framework and resource for the kinship annotation of Chinese corpora and corpus-based kinship studies.

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Aspect Based Sentiments from Tweets using Co-Ranking Multi-Modal Natural Language Processing Methodologies

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e6305.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 1061-1068

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Detection System ◽

Word Segmentation ◽

Part Of Speech Tagging ◽

Part Of Speech ◽

Sentiment Detection ◽

Analysis System ◽

Speech Tagging

Now-a-days people interest to spend their time in social sites especially twitters to post lot of tweets in every day. The posted tweets are used by many users to get the knowledge about the particular applications, products and other search engine queries. With the help of the posted tweets, their emotions and sentiments are derived which are used to get opinion about particular event. Lot of traditional sentiment detection system that has been developed but they failed to analyze huge volume of tweets and online contents with temporal patterns were also difficult to analyze. To overcome the above issues, the co-ranking multi-modal natural language processing based sentiment analysis system was developed to detect the emotions from the posted tweets. Initially, tweets of different events are collected from social sites which are processed by natural language procedures such as Stemming, Lemmatization, Part-of-speech tagging, word segmentation and parsing are applied to get the words related to posted tweets for deriving the sentiments. From the extracted emotions, co-ranking process is applied to get the opinion effectively related to particular event. Then the efficiency of the system is examined using experimental results and discussions. The introduced system recognize the sentiments from tweets with 98.80% of accuracy.

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A studyforrest extension, an annotation of spoken language in the German dubbed movie “Forrest Gump” and its audio-description

F1000Research ◽

10.12688/f1000research.27621.1 ◽

2021 ◽

Vol 10 ◽

pp. 54

Author(s):

Christian Olaf Häusler ◽

Michael Hanke

Keyword(s):

Semantic Analysis ◽

Brain Activity ◽

Real Life ◽

Semantic Space ◽

Functional Brain Imaging ◽

Part Of Speech Tagging ◽

Part Of Speech ◽

Grammatical Categories ◽

Audio Description ◽

Speech Tagging

Here we present an annotation of speech in the audio-visual movie “Forrest Gump” and its audio-description for a visually impaired audience, as an addition to a large public functional brain imaging dataset (studyforrest.org). The annotation provides information about the exact timing of each of the more than 2500 spoken sentences, 16,000 words (including 202 non-speech vocalizations), 66,000 phonemes, and their corresponding speaker. Additionally, for every word, we provide lemmatization, a simple part-of-speech-tagging (15 grammatical categories), a detailed part-of-speech tagging (43 grammatical categories), syntactic dependencies, and a semantic analysis based on word embedding which represents each word in a 300-dimensional semantic space. To validate the dataset’s quality, we build a model of hemodynamic brain activity based on information drawn from the annotation. Results suggest that the annotation’s content and quality enable independent researchers to create models of brain activity correlating with a variety of linguistic aspects under conditions of near-real-life complexity.

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An Automatic Method for Extracting Innovative Ideas Based on the Scopus® Database

KNOWLEDGE ORGANIZATION ◽

10.5771/0943-7444-2019-3-171 ◽

2019 ◽

Vol 46 (3) ◽

pp. 171-186

Author(s):

Lielei Chen ◽

Hui Fang

Keyword(s):

Citation Analysis ◽

Semantic Analysis ◽

Research Evaluation ◽

Evaluation Criterion ◽

Automatic Method ◽

Part Of Speech Tagging ◽

Part Of Speech ◽

Innovative Idea ◽

Knowledge Claims ◽

Speech Tagging

The novelty of knowledge claims in a research paper can be considered an evaluation criterion for papers to supplement citations. To provide a foundation for research evaluation from the perspective of innovativeness, we propose an automatic approach for extracting innovative ideas from the abstracts of technology and engineering papers. The approach extracts N-grams as candidates based on part-of-speech tagging and determines whether they are novel by checking the Scopus® database to determine whether they had ever been presented previously. Moreover, we discussed the distributions of innovative ideas in different abstract structures. To improve the performance by excluding noisy N-grams, a list of stop-words and a list of research description characteristics were developed. We selected abstracts of articles published from 2011 to 2017 with the topic of semantic analysis as the experimental texts. Excluding noisy N-grams, considering the distribution of innovative ideas in abstracts, and suitably combining N-grams can effectively improve the performance of automatic innovative idea extraction. Unlike co-word and co-citation analysis, innovative-idea extraction aims to identify the differences in a paper from all previously published papers.

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Lexical Rule and Lexicon Effect for Part of Speech Tagging Bahasa Madura

Matrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer ◽

10.30812/matrik.v18i1.332 ◽

2018 ◽

Vol 18 (1) ◽

pp. 65-72

Author(s):

Nindian Puspa Dewi ◽

Ubaidi Ubaidi

Keyword(s):

Text Processing ◽

Part Of Speech Tagging ◽

Pos Tagging ◽

Part Of Speech ◽

Speech Tagging ◽

Bahasa Indonesia

POS Tagging adalah dasar untuk pengembangan Text Processing suatu bahasa. Dalam penelitian ini kita meneliti pengaruh penggunaan lexicon dan perubahan morfologi kata dalam penentuan tagset yang tepat untuk suatu kata. Aturan dengan pendekatan morfologi kata seperti awalan, akhiran, dan sisipan biasa disebut sebagai lexical rule. Penelitian ini menerapkan lexical rule hasil learner dengan menggunakan algoritma Brill Tagger. Bahasa Madura adalah bahasa daerah yang digunakan di Pulau Madura dan beberapa pulau lainnya di Jawa Timur. Objek penelitian ini menggunakan Bahasa Madura yang memiliki banyak sekali variasi afiksasi dibandingkan dengan Bahasa Indonesia. Pada penelitian ini, lexicon selain digunakan untuk pencarian kata dasar Bahasa Madura juga digunakan sebagai salah satu tahap pemberian POS Tagging. Hasil ujicoba dengan menggunakan lexicon mencapai akurasi yaitu 86.61% sedangkan jika tidak menggunakan lexicon hanya mencapai akurasi 28.95 %. Dari sini dapat disimpulkan bahwa ternyata lexicon sangat berpengaruh terhadap POS Tagging.

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