• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.411-412
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• 2010

전통적으로 자연어 텍스트로부터의 감정인식 연구는 감정 키워드에 기반한다. 그러나 감정 키워드만을 이용하면 자연어 문장이 원래 갖고 있는 통사정보나 의미정보는 잃어버리게 된다. 이를 극복하기 위해 본 논문에서는 자연어 텍스트를 Predicate Logic 형태로 변환하여 감정 정보처리의 기반데이터로 사용한다. Predicate Logic형태로 변환하기 위해서 의존 문법 구문분석기를 사용하였다. 이렇게 생성된 Predicate 데이터 중 감정 정보를 갖고 있는 Predicate만을 찾아내는데 이를 위해 Emotional Predicate Dictionary를 구축하였고 이 사전에는 하나의 Predicate마다 미리 정의된 개념 클래스로 사상 시킬 수 있는 정보를 갖고 있다. 개념 클래스는 감정정보를 갖고 있는지, 어떤 감정인지, 어떤 상황에서 발생하는 감정인지에 대한 정보를 나타낸다. 자연어 텍스트가 Predicate으로 변환되고 다시 개념 클래스로 사상되고 나면 KBANN으로 구현된 Lazarus의 감정 생성 규칙에 적용시켜 최종적으로 인식된 감정을 판단한다. 실험을 통해 구현된 시스템이 인간이 인식한 감정과 약 70%이상 유사한 인식 결과를 나타냄을 보인다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.24-29
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• 2022

Semantic role labeling task used in various natural language processing fields, such as information extraction and question answering systems, is the task of identifying the arugments for a given sentence and predicate. Predicate used as semantic role labeling input are extracted using lexical analysis results such as POS-tagging, but the problem is that predicate can't extract all linguistic patterns because predicate in korean language has various patterns, depending on the meaning of sentence. In this paper, we propose a korean predicate recognition method using neural network model with pre-trained embedding models and lexical features. The experiments compare the performance on the hyper parameters of models and with or without the use of embedding models and lexical features. As a result, we confirm that the performance of the proposed neural network model was 92.63%.

• The KIPS Transactions:PartB
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• v.19B no.2
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• pp.135-148
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• 2012

This paper proposes a model for recognizing sentential paraphrases through Predicate-Argument Tuple (PAT)-based approximate alignment between two texts. We cast the paraphrase recognition problem as a binary classification by defining and applying various alignment features which could effectively express the semantic relatedness between two sentences. Experiment confirmed the potential of our approach and error analysis revealed various paraphrase patterns not being solved by our system, which can help us devise methods for further performance improvement.

• Journal of Digital Convergence
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• v.11 no.1
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• pp.225-236
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• 2013

The voice recognition has been made continuously. Now, this technology could support even natural language beyond recognition of isolated words. Interests for the voice recognition was boosting after the Siri, I-phone based voice recognition software, was presented in 2010. There are some occasions implemented voice enabled services using Korean voice recognition softwares, but their accuracy isn't accurate enough, because of background noise and lack of control on voice related features. In this paper, we propose a sort of multi-purpose preprocessor to improve this situation. This supports Keyword spotting in the continuous speech in addition to noise filtering function. This should be independent of any voice recognition software and it can extend its functionality to support continuous speech by additionally identifying the pre-predicate and the post-predicate in relative to the spotted keyword. We get validation about noise filter effectiveness, keyword recognition rate, continuous speech recognition rate by experiments.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.225-230
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• 2017

The Sijo provides dynamic rated therapeutic activities in our life. This study aims to search for the literary therapeutic function secreted from the Sijo that ends with a noun. As a result, the noun used at the final sentence secretes a predicative function. This kind of Sijo functions as twelve sound steps, even though it is condensed of just eleven sound steps with one sound step omitted. This functional secretion of Sijo is therapeutic predicate concerned with encoding of literary therapy. Thus it become possible to activate the therapeutic encoding in Sijo or a language by uttering only noun, instead of the predicate. That's because the noun in the last sentence of Sijo permeated in the human body and is done subject, and neuron of the body becomes a predicate, so that the Sijo's subject and the neuron's predicate are fused into a sentence. During the course the human body seems to recognize that the neuron's nucleus analyzes the information of the noun and makes a new sentence. This recognition might also be regarded as a process of encoding that has therapeutic functions secreted from the human body.

• ETRI Journal
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• v.44 no.3
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• pp.438-449
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• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.459-472
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• 2011

The vast number of biomedical literature is an important source of biomedical interaction information discovery. However, it is complicated to obtain interaction information from them because most of them are not easily readable by machine. In this paper, we present a method for extracting biomedical interaction information assuming that the biomedical Named Entities (NEs) are already identified. The proposed method labels all possible pairs of given biomedical NEs as INTERACTION or NO-INTERACTION by using a Maximum Entropy (ME) classifier. The features used for the classifier are obtained by applying various NLP techniques such as POS tagging, base phrase recognition, parsing and predicate-argument recognition. Especially, specific verb predicates (activate, inhibit, diminish and etc.) and their biomedical NE arguments are very useful features for identifying interactive NE pairs. Based on this, we devised a twostep method: 1) an interaction verb extraction step to find biomedically salient verbs, and 2) an argument relation identification step to generate partial predicate-argument structures between extracted interaction verbs and their NE arguments. In the experiments, we analyzed how much each applied NLP technique improves the performance. The proposed method can be completely improved by more than 2% compared to the baseline method. The use of external contextual features, which are obtained from outside of NEs, is crucial for the performance improvement. We also compare the performance of the proposed method against the co-occurrence-based and the rule-based methods. The result demonstrates that the proposed method considerably improves the performance.