Similarity-Driven Semantic Role Induction via Graph Partitioning
نویسندگان
چکیده
منابع مشابه
Similarity-Driven Semantic Role Induction via Graph Partitioning
As in many natural language processing tasks, data-driven models based on supervised learning have become the method of choice for semantic role labeling. These models are guaranteed to perform well when given sufficient amount of labeled training data. Producing this data is costly and time-consuming, however, thus raising the question of whether unsupervised methods offer a viable alternative...
متن کاملUnsupervised Semantic Role Induction with Graph Partitioning
In this paper we present a method for unsupervised semantic role induction which we formalize as a graph partitioning problem. Argument instances of a verb are represented as vertices in a graph whose edge weights quantify their role-semantic similarity. Graph partitioning is realized with an algorithm that iteratively assigns vertices to clusters based on the cluster assignments of neighboring...
متن کاملComputing Semantic Clusters by Semantic Mirroring and Spectral Graph Partitioning
Using the technique of semantic mirroring a graph is obtained that represents words and their translations from a parallel corpus or a bilingual lexicon. The connectedness of the graph holds information about the semantic relations of words that occur in the translations. Spectral graph theory is used to partition the graph, which leads to a grouping of the words in different clusters. We illus...
متن کاملSemi-supervised Semantic Role Labeling via Graph-Alignment
Semantic roles, which constitute a shallow form of meaning representation, have attracted increasing interest in recent years. Various applications have been shown to benefit from this level of semantic analysis, and a large number of publications has addressed the problem of semantic role labeling, i.e., the task of automatically identifying semantic roles in arbitrary sentences. A major limit...
متن کاملTexts semantic similarity detection based graph approach
Similarity of text documents is important to analyze and extract useful information from text documents and generation of the appropriate data. Several cases of lexical matching techniques offered to determine the similarity between documents that have been successful to a certain limit and these methods are failing to find the semantic similarity between two texts. Therefore, the semantic simi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Linguistics
سال: 2014
ISSN: 0891-2017,1530-9312
DOI: 10.1162/coli_a_00195