Question Classification using Semantic, Syntactic and Lexical features
نویسندگان
چکیده
منابع مشابه
Readability Classification for German using Lexical, Syntactic, and Morphological Features
We investigate the problem of reading level assessment for German texts on a newly compiled corpus of freely available easy and difficult articles, targeted at adult and child readers respectively. We adapt a wide range of syntactic, lexical and language model features from previous research on English and combined them with new features that make use of the rich morphology of German. We show t...
متن کاملAnswerfinder: Question Answering by Combining Lexical, Syntactic and Semantic Information
We present a question answering system that combines information at the lexical, syntactic, and semantic levels, in the process to find and rank the candidate answer sentences. The candidate exact answers are extracted from the candidate answer sentences by means of a combination of information-extraction techniques (named entity recognition) and patterns based on logical forms. The system part...
متن کاملCU-TMP: Temporal Relation Classification Using Syntactic and Semantic Features
We approached the temporal relation identification tasks of TempEval 2007 as pair-wise classification tasks. We introduced a variety of syntactically and semantically motivated features, including temporal-logicbased features derived from running our Task B system on the Task A and C data. We trained support vector machine models and achieved the second highest accuracies on the tasks: 61% on T...
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 کاملCombining Lexical, Syntactic, and Semantic Evidence for Textual Entailment Classification
This paper describes the Emory system for recognizing textual entailment as used for the RTE4 track at the TAC 2008 competition. We use a supervised machine learning approach to train a classifier over a variety of lexical, syntactic, and semantic metrics. We treat the output of each metric as a feature, and train a classifier on the provided data from the previous RTE tracks. As a result, our ...
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: International journal of Web & Semantic Technology
سال: 2013
ISSN: 0976-2280,0975-9026
DOI: 10.5121/ijwest.2013.4304