Student name: Gopal Prabath Chelikani
UTA ID: 1002024881
A Pattern and POS Auto-Learning Method for Terminology Extraction from Scientific
Text
https://www.sciencedirect.com/science/article/pii/S2543925122000031
A lot of research papers were published day to day, it’s impossible to quickly find new words and
meaning from documents, most of work are based on data, quite difficult to deal with unlabelled data.
By utilizing methods based on part of speech and few initial learnable patters to obtain initial
terminology tokens an pos sequences. Learnable patterns were used to obtain terminology token and
pos sequences. Patterns were constructed more sentence to find pos sequence of terminology. It’s
difficult for researchers to discover something by reading papers. Automatic indexing knowledge
management, way of new technologies relies based on entity recognition. Results on training and
testing data ,learning models with training data, scientific texts has extraction for labels.. pattern and
pos autolearning method has proposed to overcome to extract sentences and terminologies.
pos sequences in sentences if matched utilize particular words. Terminologies on scientific sentences
is extracted. Pattern and pos auto learning method for extraction of terminology from unlabelled data
in different fields. Incomparision with supervised method, unsupervised can do more extraction of
terminology entities without annotations. Unsupervised method on pos sequence and sentence
patterns to extract scientific text from entities. Terminologies were extracted from unlabelled
scientific texts, surrounding words and pos sequence of terminologies were considered. Sentence
pattern for extract target from sentence string is regular expression.
Input data were taken and tools were used to get pos sequence of sentences. It is designed regular
expression, terminology string from matched groups, suitable terminology tokens and pos sequence
are received as output. Pos sequence is considered as extracted terminology pos sequence. New found
patterns were used but not matched sentences. Sentence will move to unextracted sentence base.to
generate new pattern candidate word this chosen from sentence. New patterns are generated for
results and patterns to pattern base. Initial terminology for extraction loop.
Terminology word is obtained is matched with patterns it must be clean-up for suitable pos sequence.
Discard some token which has little valid information.pos sequence for token index are continuous,
to evaluate quality and discard one. New generated sequence patterns were used as key. It generates
certain sentences for percentage. If number of matched is larger than threshold. Sentence patterns
and pos sequence of unlabelled data for new terminologies. Sentences for new technologies when
string is input. We can pos sequence to match pos sequence for more accurate result.
For the performance scientific abstracts from web were crawled, these abstracts were from different
domains such as machine learning, big data. As for pre-processing NLTK split into sentences and into
tokens. StanfordNLP to get pos tags and relations of cut sequences. Data consist of abstracts,
tokenized sentences, pos tags and dependency relations.
Tokenized sentences of abstract and pos tags. we use 54k+ sentences and their POS sequences as training
data without labels and 500 sentences and their POS sequences as test data with labels. A labelled sentence
consists of a token sequence and a label sequence. When the token is a terminology word, the
corresponding label is 1. Otherwise, the label is 0. Manually annotate the 500 sentences in the test set to
ensure the accuracy of the annotation.
Precision and f1 score for metrics for comparison method with two rule based methods on dataset. For
unsupervised learning methods, extraction results were full of noisy terms. Hard standard evaluation is
done it is difficult to compare these unsupervised methods. If extraction result has overlap with real
terminology, extracted is considered as valid result.
If number of word in extracted term and term in terminology at sentence at same time exceeds product
between number of words in terminology term and cover percentage comes to conclusion that extracted
term is correct. For rule 1based method continuous nouns as terminology terms, and 2 choosed to filter
noun and non-verb and use left word with continuous indexes in sequence. Each case has several words
and forms list of words terminology is displayed in blue in each sentence. The word is extracted using the
methodology and data present in the data sets were cleansed and finally the terminologies were displayed
in blue color. It has good performance on method words, performance is lower when comes to professional
technologies.
Conclusion:For extracting terminologies from the list of scientific words, cold start method on sentence pattern and
pos sequence is proposed so that this method will extract terminologies without learning on data that is
labelled and senentece patterns were used to initial cold start. It has new patterns and unlabelled data on
POS sequences. These patterns and pos sequences for new terminology extraction from new scientific
sentences. Paper abstract sentences from knowledge of web shows that this method has good recall,
precision and f1 scores on test data. This approach is for unlabelled data extraction.