Semantic Role Labeling with Maximum Entropy Classifier
نویسندگان
چکیده
منابع مشابه
Semantic Role Labeling System Using Maximum Entropy Classifier
A maximum entropy classifier is used in our semantic role labeling system, which takes syntactic constituents as the labeling units. The maximum entropy classifier is trained to identify and classify the predicates’ semantic arguments together. Only the constituents with the largest probability among embedding ones are kept. After predicting all arguments which have matching constituents in ful...
متن کاملMaximum Entropy Based Semantic Role Labeling
The semantic role labeling (SRL) refers to finding the semantic relation (e.g. Agent, Patient, etc.) between a predicate and syntactic constituents in the sentences. Especially, with the argument information of the predicate, we can derive the predicateargument structures, which are useful for the applications such as automatic information extraction. As previous work on the SRL, there have bee...
متن کاملSemantic Role Labeling using Maximum Entropy Model
In this paper, we propose a semantic role labeling method using a maximum entropy model, which enables not only to exploit rich features but also to alleviate the data sparseness problem in a well-founded model. For applying the maximum entropy model to semantic role labeling, we take a incremental approach as follows: firstly, the semantic roles are assigned to the arguments in the immediate c...
متن کاملSemantic Role Labeling of NomBank: A Maximum Entropy Approach
This paper describes our attempt at NomBank-based automatic Semantic Role Labeling (SRL). NomBank is a project at New York University to annotate the argument structures for common nouns in the Penn Treebank II corpus. We treat the NomBank SRL task as a classification problem and explore the possibility of adapting features previously shown useful in PropBank-based SRL systems. Various NomBank-...
متن کاملSemantic role labeling with Boosting, SVMs, Maximum Entropy, SNOW, and Decision Lists
This paper describes the HKPolyU-HKUST systems which were entered into the Semantic Role Labeling task in Senseval-3. Results show that these systems, which are based upon common machine learning algorithms, all manage to achieve good performances on the non-restricted Semantic Role Labeling task.
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: Journal of Software
سال: 2007
ISSN: 1000-9825
DOI: 10.1360/jos180565