• Journal of KIISE
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• v.42 no.2
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• pp.220-226
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• 2015

Semantic role labeling (SRL) systems determine the semantic role labels of the arguments of predicates in natural language text. An SRL system usually needs to perform four tasks in sequence: Predicate Identification (PI), Predicate Classification (PC), Argument Identification (AI), and Argument Classification (AC). In this paper, we use the Korean Propbank to develop our Korean semantic role labeling system. We describe our Korean semantic role labeling system that uses sequence labeling with structured Support Vector Machine (SVM). The results of our experiments on the Korean Propbank dataset reveal that our method obtains a 97.13% F1 score on Predicate Identification and Classification (PIC), and a 76.96% F1 score on Argument Identification and Classification (AIC).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.3
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• pp.134-138
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• 2003

The purpose of this study is to analyse the argument alternations in terms of semantic perspective. Argument alternation has long been an interesting topic for the linguists regardless of their linguistic schools. Semantic analysis of argument alternation is attempted by Dowty(2001) based on the Levin(1993)'s classification. The study is focused on the phenomenon where meaning changes with argument alternations even though those sentences look the same syntactically and lineally. 1 tried not only to classify verbs according to the meaning changes but to explain the alternations in semantic point of view. The verbs are divided into 4 types- Touch type, Hit type, Cut type, and Break type. Each type of verbs are tested if they show special characteristics with three alternations-Middle alternation, Body-part possessor Ascension, and Conative Alternation. And semantic analysis is tried based on that classification.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.531-540
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• 2010

In this paper, we present a two-phase shallow semantic parsing method based on a maximum entropy model. The first phase is to recognize semantic arguments, i.e., argument identification. The second phase is to assign appropriate semantic roles to the recognized arguments, i.e., argument classification. Here, the performance of the first phase is crucial for the success of the entire system, because the second phase is performed on the regions recognized at the identification stage. In order to improve performances of the argument identification, we incorporate syntactic knowledge into its pre-processing step. More precisely, boundaries of the immediate clause and the upper clauses of a predicate obtained from clause identification are utilized for reducing the search space. Further, the distance on parse trees from the parent node of a predicate to the parent node of a parse constituent is exploited. Experimental results show that incorporation of syntactic knowledge and the separation of argument identification from the entire procedure enhance performances of the shallow semantic parsing system.

• Korean Linguistics
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• v.48
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• pp.223-268
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• 2010

The purpose of this paper is to build a semantic networks of the 'cooking class' verb (based on 'CoreNet' of KAIST). This proceedings needs to adjust the concept classification. Then sub-categories of [Cooking] and [Foodstuff] hierarchy of CoreNet was adjusted for the construction of verb semantic networks. For the building a semantic networks, each meaning of 'Cooking verbs' of Korean has to be analyzed. This paper focused on the Korean 'heating' verbs and 'non-heating'verbs. Case frame structure and argument information were inserted for the describing verb information. This paper use a Propege 3.3 as a tool for building "cooking verb" semantic networks. Each verb and noun was inserted into it's class, and connected by property relation marker 'HasThemeAs', 'IsMaterialOf'.

• Language and Information
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• v.9 no.1
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• pp.1-18
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• 2005

In Korean terminologies, adjectival terms are rare, and the meaning and function associated with adjectives in Indo-European languages are often realized instead in noun form. However, the rarer adjectival terms we, the more they are used in restrictive and repetitive ways in specialized domains. Thus, it is important to distinguish the different senses of these terms. In this work, focusing on semantic modeling in terminology, we distinguish the different senses of adjectival medical terms by applying lexico-semantic criteria (L'Homme, 2004a) and by classifying the semantic category of the arguments of the adjective (Bae and others, 2002). The result not only contributes to enriching medical terminology, but also empirically demonstrates a method for distinguishing the different senses of adjectival medical terms. In this work, we obtained an average of 1.854 senses for each term. We used the KAIST corpus, composed of medical texts (1,500,000 eojeols), and a group of texts on various subjects (40,000,000 eojeols)

• 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.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.85-92
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• 2010

A shallow semantic parsing system analyzes the relationship that a syntactic constituent of the sentence has with a predicate. It identifies semantic arguments representing agent, patient, instrument, etc. of the predicate. In this study, we propose a two-phase shallow semantic parsing model which consists of the identification phase and the classification phase. We first find the boundary of semantic arguments from partial syntactic parsing results, and then assign appropriate semantic roles to the identified semantic arguments. By taking the sequential two-phase approach, we can alleviate the unbalanced class distribution problem, and select the features appropriate for each task. Experiments show the relative contribution of each phase on the test data.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.263-271
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• 2008

This paper presents a Korean conversational agent system in a mobile environment using natural language processing techniques. The aim of a conversational agent in mobile environment is to provide natural language interface and enable more natural interaction between a human and an agent. Constructing such an agent, it is required to develop various natural language understanding components and effective utterance generation methods. To understand spoken style utterance, we perform morphosyntactic analysis, shallow semantic analysis including modality classification and predicate argument structure analysis, and to generate a system utterance, we perform example based search which considers lexical similarity, syntactic similarity and semantic similarity.

• Language and Information
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• v.18 no.1
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• pp.33-52
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• 2014

This paper proposes a unified structural account of some instances of the English Modals and Semi-auxiliaries. The classification and the syntactic/structural description of the English Modal auxiliary verbs and verb-related elements have long been the center for many proposals in the history of generative syntax. According to van Gelderen (1993) and Lightfoot (2002), it was sometime around 1380 that the Tense-node (T) appeared in the phrasal structures of the English language, and the T-node is under which the English Modal auxiliaries occupy. Closely related is the existing evidence that English Modals were used as main verbs up to the early sixteenth century (Lightfoot 1991, Han 2000). This paper argues for a bi-clausal approach to English Modal auxiliaries with the infinitival particle 'to' such as 'ought to' 'used to' and 'dare (to)' 'need (to)', etc. and Semi-auxiliaries including 'be to' and 'have to'. More specifically, 'ought' in 'ought to' constructions, for instance, undergoes V-to-T movement within the matrix clause, just like 'HAVEAux' and all instances of 'BE', whereas 'to' occupies the T position of the embedded complement clause. By proposing the bi-clausal account, Radford's (2004, 2009) problems can be solved. Further, the historical motivation for the account takes a stance along with Norde (2009) and Brinton & Traugott (2005) in that Radford's (2004, 2009) syncretization of the two positions of the infinitival particle 'to' is no different from the 'boundary loss' in the process of Grammariticalization. This line of argument supports Krug's (2011), and in turn Bolinger's(1980) generalization on Auxiliaryhood, while providing a novel insight into Head movement of V-to-T in Present Day English.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.131-150
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• 2015

Omission of noun phrases for obligatory cases is a common phenomenon in sentences of Korean and Japanese, which is not observed in English. When an argument of a predicate can be filled with a noun phrase co-referential with the title, the argument is more easily omitted in Encyclopedia texts. The omitted noun phrase is called a zero anaphor or zero pronoun. Encyclopedias like Wikipedia are major source for information extraction by intelligent application systems such as information retrieval and question answering systems. However, omission of noun phrases makes the quality of information extraction poor. This paper deals with the problem of developing a system that can restore omitted noun phrases in encyclopedia documents. The problem that our system deals with is almost similar to zero anaphora resolution which is one of the important problems in natural language processing. A noun phrase existing in the text that can be used for restoration is called an antecedent. An antecedent must be co-referential with the zero anaphor. While the candidates for the antecedent are only noun phrases in the same text in case of zero anaphora resolution, the title is also a candidate in our problem. In our system, the first stage is in charge of detecting the zero anaphor. In the second stage, antecedent search is carried out by considering the candidates. If antecedent search fails, an attempt made, in the third stage, to use the title as the antecedent. The main characteristic of our system is to make use of a structural SVM for finding the antecedent. The noun phrases in the text that appear before the position of zero anaphor comprise the search space. The main technique used in the methods proposed in previous research works is to perform binary classification for all the noun phrases in the search space. The noun phrase classified to be an antecedent with highest confidence is selected as the antecedent. However, we propose in this paper that antecedent search is viewed as the problem of assigning the antecedent indicator labels to a sequence of noun phrases. In other words, sequence labeling is employed in antecedent search in the text. We are the first to suggest this idea. To perform sequence labeling, we suggest to use a structural SVM which receives a sequence of noun phrases as input and returns the sequence of labels as output. An output label takes one of two values: one indicating that the corresponding noun phrase is the antecedent and the other indicating that it is not. The structural SVM we used is based on the modified Pegasos algorithm which exploits a subgradient descent methodology used for optimization problems. To train and test our system we selected a set of Wikipedia texts and constructed the annotated corpus in which gold-standard answers are provided such as zero anaphors and their possible antecedents. Training examples are prepared using the annotated corpus and used to train the SVMs and test the system. For zero anaphor detection, sentences are parsed by a syntactic analyzer and subject or object cases omitted are identified. Thus performance of our system is dependent on that of the syntactic analyzer, which is a limitation of our system. When an antecedent is not found in the text, our system tries to use the title to restore the zero anaphor. This is based on binary classification using the regular SVM. The experiment showed that our system's performance is F1 = 68.58%. This means that state-of-the-art system can be developed with our technique. It is expected that future work that enables the system to utilize semantic information can lead to a significant performance improvement.