A Token Centric Part-of-Speech Tagger for Biomedical Text

2011 ◽  
pp. 317-326
Author(s):  
Neil Barrett ◽  
Jens Weber-Jahnke
Keyword(s):  
Biomedical Text ◽  
Part Of Speech

scholarly journals MedPost: a part-of-speech tagger for bioMedical text

2004 ◽  
Vol 20 (14) ◽  
pp. 2320-2321 ◽  
Author(s):  
L. Smith ◽  
T. Rindflesch ◽  
W. J. Wilbur
Keyword(s):  
Biomedical Text ◽  
Part Of Speech

scholarly journals A comparison of parsing technologies for the biomedical domain

2005 ◽  
Vol 11 (1) ◽  
pp. 27-65 ◽  
Author(s):  
CLAIRE GROVER ◽  
ALEX LASCARIDES ◽  
MIRELLA LAPATA
Keyword(s):  
Biomedical Text ◽  
Biomedical Domain ◽  
Syntactic Complexity ◽  
Hospital Arrival ◽  
Trade Off ◽  
Part Of Speech ◽  
Compound Nouns ◽  
Patient Arrival ◽  
The One ◽  
Processing Techniques

This paper reports on a number of experiments which are designed to investigate the extent to which current NLP resources are able to syntactically and semantically analyse biomedical text. We address two tasks: (a) parsing a real corpus with a hand-built wide-coverage grammar, producing both syntactic analyses and logical forms and (b) automatically computing the interpretation of compound nouns where the head is a nominalisation (e.g. hospital arrival means an arrival at hospital, while patient arrival means an arrival of a patient). For the former task we demonstrate that flexible and yet constrained pre-processing techniques are crucial to success: these enable us to use part-of-speech tags to overcome inadequate lexical coverage, and to package up complex technical expressions prior to parsing so that they are blocked from creating misleading amounts of syntactic complexity. We argue that the XML-processing paradigm is ideally suited for automatically preparing the corpus for parsing. For the latter task, we compute interpretations of the compounds by exploiting surface cues and meaning paraphrases, which in turn are extracted from the parsed corpus. This provides an empirical setting in which we can compare the utility of a comparatively deep parser vs. a shallow one, exploring the trade-off between resolving attachment ambiguities on the one hand and generating errors in the parses on the other. We demonstrate that a model of the meaning of compound nominalisations is achievable with the aid of current broad-coverage parsers.

A token centric part-of-speech tagger for biomedical text

2014 ◽  
Vol 61 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Neil Barrett ◽  
Jens Weber-Jahnke
Keyword(s):  
Biomedical Text ◽  
Part Of Speech

Developing a Robust Part-of-Speech Tagger for Biomedical Text

2005 ◽  
pp. 382-392 ◽  
Author(s):  
Yoshimasa Tsuruoka ◽  
Yuka Tateishi ◽  
Jin-Dong Kim ◽  
Tomoko Ohta ◽  
John McNaught ◽  
...  
Keyword(s):  
Biomedical Text ◽  
Part Of Speech

Identification and Transformation of Terminal Morphemes in Medical English

1969 ◽  
Vol 8 (02) ◽  
pp. 84-90 ◽  
Author(s):  
A. W. Pratt ◽  
M. Pacak
Keyword(s):  
Word Form ◽  
Subsequent Transformation ◽  
Part Of Speech ◽  
Morphological Segmentation ◽  
Medical English ◽  
Nominal Form ◽  
Admissible Path ◽  
Near Future ◽  
The Given ◽  
Admissible Sequences

The system for the identification and subsequent transformation of terminal morphemes in medical English is a part of the information system for processing pathology data which was developed at the National Institutes of Health.The recognition and transformation of terminal morphemes is restricted to classes of adjectivals including the -ING and -ED forms, nominals and homographic adjective/noun forms.The adjective-to-noun and noun-to-noun transforms consist basically of a set of substitutions of adjectival and certain nominal suffixes by a set of suffixes which indicate the corresponding nominal form(s).The adjectival/nominal suffix has a polymorphosyntactic transformational function if it has the property of being transformed into more than one nominalizing suffix (e.g., the adjectival suffix -IC can be substituted by a set of nominalizing suffixes -Ø, -A, -E, -Y, -IS, -IA, -ICS): the adjectival suffix has a monomorphosyntactic transformational property if there is only one admissible transform (e.g., -CIC → -X).The morphological segmentation and the subsequent transformations are based on the following principles:a. The word form is segmented according to the principle of »double consonant cut,« i.e., terminal characters following the last set of double consonants are analyzed and treated as a potential suffix. For practical purposes only such terminal suffixes of a maximum length of four have been analyzed.b. The principle that the largest segment of a word form common to both adjective and noun or to both noun stems is retained as a word base for transformational operations, and the non-identical segment is considered to be a »suffix.«The backward right-to-left character search is initiated by the identification of the terminal grapheme of the given word form and is extended to certain admissible sequences of immediately preceding graphemes.The nodes which represent fixed sequences of graphemes are labeled according to their recognition and/or transformation properties.The tree nodes are divided into two groups:a. productive or activatedb. non-productive or non-activatedThe productive (activated) nodes are sequences of sets of graphemes which possess certain properties, such as the indication about part-of-speech class membership, the transformation properties, or both. The non-productive (non-activated) nodes have the function of connectors, i.e., they specify the admissible path to the productive nodes.The computer program for the identification and transformation of the terminal morphemes is open-ended and is already operational. It will be extended to other sub-fields of medicine in the near future.

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

A BRIEF ANALYSIS OF TIME-PREPOSITIONS IN THE RUSSIAN LANGUAGE

2019 ◽  
Vol 18 (28) ◽  
pp. 187-195
Author(s):  
Yabing Zhang
Keyword(s):  
Foreign Language ◽  
Foreign Students ◽  
Russian Language ◽  
Parts Of Speech ◽  
Organic Structure ◽  
Part Of Speech ◽  
True Meaning ◽  
Oral Speech ◽  
The Russian Language

This article is devoted to the problem of using Russian time-prepositions by foreigners, especially by the Chinese. An analysis of modern literature allows the author to identify the main areas of the work aimed at foreign students’ development of the skills and abilities to correctly build the prepositional combinations and continuously improve the communication skills by means of the Russian language. In this paper, the time-prepositions in the Russian language have been analyzed in detail; some examples of polysemantic use of prepositions, their semantic and stylistic shades alongside with possible errors made by foreign students are presented. The results of the study are to help in developing a system of teaching Russian time-prepositions to a foreign language audience, taking into account their native language, on the basis of the systemic and functional, communicative and activity-centred basis. The role of Russian time-prepositions in constructing word combinations has been identified; the need for foreign students’ close attention to this secondary part of speech has been specified. It has been stated that prepositions are the most dynamic and open type of secondary language units within the quantitative and qualitative composition of which regular changes take place. The research substantiates the need that students should be aware of the function of time-preposition in speech; they are to get acquainted with the main time-prepositions and their meanings, to distinguish prepositions and other homonymous parts of speech as well as to learn stylistic shades of time-prepositions. Some recommendations related to the means of mastering time-prepositions have been given: to target speakers to assimilate modern literary norms and, therefore, to teach them how to choose and use them correctly by means of linguistic keys that are intended to fill the word with true meaning, to give it an organic structure, an inherent form and an easy combinability in the texts and oral speech.

THE STUDY OF COMPLEMENTARY COMPLEXES IN MODERN GRAMMAR SCHOOLS

2019 ◽  
pp. 75-86
Author(s):  
O. Tuhai
Keyword(s):  
Noun Phrases ◽  
Internal Argument ◽  
Subordinate Clauses ◽  
Part Of Speech ◽  
Theoretical Approaches ◽  
The Status ◽  
Clausal Complementation ◽  
Communicative Approach ◽  
Clausal Complements ◽  
Form Feature

The article focuses on the basic theoretical approaches to the analysis of complementary complexes in modern grammar paradigms. The phenomenon of clausal complementation has been presented. Subordinate sentences are characterized as object clausal complements with the status of a core internal argument of the main predicate. Grammatical configuration and functioning of finite/infinitive complementary sentences in English have been revealed. Grammatical status of clauses under the study is postulated as object predication or the internal verbal complement in the function of an object. Grammatical indicators of finite sentences are analyzed considering specific that/wh- markers of complementation, semantics of matrix verbs as well as temporal tense-form feature in a verbal phrase. Grammatical configuration of infinitive sentences is denoted by to-/wh-markers and noun phrases in a certain case. Identifying criteria of verbal clausal complements have been distinguished. Morphology of the predicate, internal/external syntax of a complementary construction are grounded as leading features of their definition. Typology of verbal complementation in terms of transitivity, complement attachment to the perculia part of speech, functional communicative approach has been reviewed. General monotransitive, complex-transitive and ditransitive complementation has been outlined. When being attached to a particular language constituent a clause is determined as nominal, adjective or verbal complement. Due to communicative peculiarity finite subordinate clauses are positioned as content declarative, interrogative and exclamative.

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

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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