On the Effectiveness of Using Syntactic and Shallow Semantic Tree Kernels for Automatic Assessment of Essays
نویسندگان
چکیده
This paper is concerned with the problem of automatic essay grading, where the task is to grade student written essays given course materials and a set of humangraded essays as training data. Latent Semantic Analysis (LSA) has been used extensively over the years to accomplish this task. However, the major limitation of LSA is that it only retains the frequency of words by disregarding the word sequence, and the syntactic and semantic structure of texts. As a remedy, we propose the use of syntactic and shallow semantic tree kernels for grading essays. Experiments suggest that syntactic and semantic structural information can significantly improve the performance of the state-of-the-art LSAbased models for automatic essay grading.
منابع مشابه
برچسب‌زنی خودکار نقش‌های معنایی در جملات فارسی به کمک درخت‌های وابستگی
Automatic identification of words with semantic roles (such as Agent, Patient, Source, etc.) in sentences and attaching correct semantic roles to them, may lead to improvement in many natural language processing tasks including information extraction, question answering, text summarization and machine translation. Semantic role labeling systems usually take advantage of syntactic parsing and th...
متن کاملبرچسبزنی نقش معنایی جملات فارسی با رویکرد یادگیری مبتنی بر حافظه
Abstract Extracting semantic roles is one of the major steps in representing text meaning. It refers to finding the semantic relations between a predicate and syntactic constituents in a sentence. In this paper we present a semantic role labeling system for Persian, using memory-based learning model and standard features. Our proposed system implements a two-phase architecture to first identify...
متن کاملExploiting Syntactic and Shallow Semantic Kernels for Question Answer Classification
We study the impact of syntactic and shallow semantic information in automatic classification of questions and answers and answer re-ranking. We define (a) new tree structures based on shallow semantics encoded in Predicate Argument Structures (PASs) and (b) new kernel functions to exploit the representational power of such structures with Support Vector Machines. Our experiments suggest that s...
متن کاملUsing Syntactic and Shallow Semantic Kernels to Improve Multi-Modality Manifold-Ranking for Topic-Focused Multi-Document Summarization
Multi-modality manifold-ranking is recently used successfully in topic-focused multi-document summarization. This approach is based on Bag-Of-Words (BOW) assumption where the pair-wise similarity values between sentences are computed using the standard cosine similarity measure (TF*IDF). However, the major limitation of the TF*IDF approach is that it only retains the frequency of the words and ...
متن کاملPresentation of an efficient automatic short answer grading model based on combination of pseudo relevance feedback and semantic relatedness measures
Automatic short answer grading (ASAG) is the automated process of assessing answers based on natural language using computation methods and machine learning algorithms. Development of large-scale smart education systems on one hand and the importance of assessment as a key factor in the learning process and its confronted challenges, on the other hand, have significantly increased the need for ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013