Automatic Semantic Role Labeling Research Articles

Semantic Role Labeling System for Persian Language

In this article, we present an automatic semantic role labeling system in Persian consisting of two modules: argument identification for specifying argument spans and argument classification for categorizing their semantic roles. Our modules have been trained on Persian Proposition Bank in which predicate-argument information is manually added as a layer on top of Persian Dependency Treebank with about 30,000 sentences. Therefore, our system was trained on 216,871 verbal predicates and 42,386 nonverbal ones consisting of 40,813 nouns and 1,573 adjectives with 33 semantic classes. As a supervised method, we used maximum entropy for building an argument identifier that results in human-level accuracy of 99% and support vector machine for an argument classifier with an F1 of 84. Regarding both verbal and nonverbal predicates with an expanded role set, we achieved reasonable results.

Semantic Role Labeling of English Tweets

Semantic role labeling (SRL) is a task of defining the conceptual role to the arguments of predicate in a sentence. This is an important task for a wide range of tweet related applications associated with semantic information extraction. SRL is a challenging task due to the difficulties regarding general semantic roles for all predicates. It is more challenging for Social Media Text (SMT) where the nature of text is more casual. This paper presents an automatic SRL system for English tweets based on Sequential Minimal Optimization (SMO) algorithm. Proposed system is evaluated through experiments and reports comparable performance with the prior state-of-the art SRL system.

Integration of Korean Feature Information for SRL System

Semantic role labeling is typically used to resolve a problem from the perspective of classification by using a corpus. Semantic Role Labeling determines an adequate predicate-argument relation. It can be used to improve performance in various areas of natural language processing. In this paper, automatic semantic role labeling using 10,000 sentences in a semantic role tagged corpus constructed from a Korean syntax tagged corpus was conducted. In Korean, affix, such as josa and eomi, is a very important role in syntactic parsing and semantic role labeling. Semantic role labeling was achieved in this study by improving particle and word ending information, which were insufficiently addressed in previous studies on semantic role labeling, and creating new features. When features based on the affix information created in this study were added to the basic features used in previous studies on semantic role labeling of languages, an F1 score of approximately 80.83% was obtained.

Generalizing the semantic roles in the Chinese Proposition Bank

The Chinese Proposition Bank (CPB) is a corpus annotated with semantic roles for the arguments of verbal and nominalized predicates. The semantic roles for the core arguments are defined in a predicate-specific manner. That is, a set of semantic roles, numerically identified, are defined for each sense of a predicate lemma and recorded in a valency lexicon called frame files. The predicate-specific manner in which the semantic roles are defined reduces the cognitive burden on the annotators since they only need to internalize a few roles at a time and this has contributed to the consistency in annotation. It was also a sensible approach given the contentious issue of how many semantic roles are needed if one were to adopt of set of global semantic roles that apply to all predicates. A downside of this approach, however, is that the predicate-specific roles may not be consistent across predicates, and this inconsistency has a negative impact on training automatic systems. Given the progress that has been made in defining semantic roles in the last decade or so, time is ripe for adopting a set of general semantic roles. In this article, we describe our effort to "re-annotate" the CPB with a set of "global" semantic roles that are predicate-independent and investigate their impact on automatic semantic role labeling systems. When defining these global semantic roles, we strive to make them compatible with a recently published ISO standards on the annotation of semantic roles (ISO 24617-4:2014 SemAF-SR) while taking the linguistic characteristics of the Chinese language into account. We show that in spite of the much larger number of global semantic roles, the accuracy of an off-the-shelf semantic role labeling system retrained on the data re-annotated with global semantic roles is comparable to that trained on the data set with the original predicate-specific semantic roles. We also argue that the re-annotated data set, together with the original data, provides the user with more flexibility when using the corpus.

Generalization of Semantic Roles in Automatic Semantic Role Labeling

FrameNet，PropBank といった意味タグ付きコーパスの出現とともに，機械学習の枠組みを利用した自動意味役割付与システムが数多く研究されてきた．しかし，これらのコーパスは個々のフレームに固有の意味役割を定義するため，コーパス中に低頻度，或いは未出現の意味役割が数多く存在し，効率的な学習を妨げている．本論文は，意味役割付与における意味役割の汎化問題を取り上げ，既存の汎化指標と新たに提案する指標を役割の分類精度を通して比較し，それぞれの特徴を探求する．また，複数の汎化指標を同時に利用する分類モデルが自動意味役割付与の精度を向上させることを示す．実験では，FrameNet において全体の精度で 19.16% のエラー削減，F1 マクロ平均で 7.42% の向上を，PropBank において全体の精度で 24.07% のエラー削減，未知動詞に対するテストで 26.39% のエラー削減を達成した．

Generalization of Semantic Roles in Automatic Semantic Role Labeling

Generalization of Semantic Roles in Automatic Semantic Role Labeling

自動意味役割付与における意味役割の汎化

自動意味役割付与における意味役割の汎化

Semantic Role Labeling

This book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard stages and feature choices, as well as giving details of several specific systems. Recent advances include the use of joint inference to take advantage of context sensitivities, and attempts to improve performance by closer integration of the syntactic parsing task with semantic role labeling. Chapter 3 also discusses the impact the granularity of the semantic roles has on system performance. Having outlined the basic approach with respect to English, Chapter 4 goes on to discuss applying the same techniques to other languages, using Chinese as the primary example. Although substantial training data is available for Chinese, this is not the case for many other languages, and techniques for projecting English role labels onto parallel corpora are also presented.