[PDF] Top 20 Text to Text Semantic Similarity for Automatic Short Answer Grading
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Text to Text Semantic Similarity for Automatic Short Answer Grading
... human grading of the data set to an accept/reject annotation by us- ing a threshold of ...Every answer with a grade higher than ...ery answer below ... See full document
9
Short Text Semantic Similarity using Glove Word Embedding
... of text. Semantic textual similarity (STS) captures the notion that some text are more similar than others by measuring the texts degree of semantic ...the similarity scores were ... See full document
6
Using Part-of-Speech Tags as Deep-Syntax Indicators in Determining Short-Text Semantic Similarity
... the semantic similarity of two words: the topological or knowledge-based one, which uses expert knowledge, and the statistical or corpus-based one, which uses a text ...Topological similarity ... See full document
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Distributed Vector Representations for Unsupervised Automatic Short Answer Grading
... of automatic short answer grading, evaluating a collection of approaches inspired by recent advances in distributional text ...determining text similarity using ... See full document
10
Wisdom of Students: A Consistent Automatic Short Answer Grading Technique
... semantic similarity measures for ASAG including knowledge-based measures using Wordnet as well as vector space-based measures such as Latent Semantic Analysis (LSA) (Landauer et ...Explicit ... See full document
10
A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading
... Towards addressing this issue, we exploit a fact that student answers to a question, as a collection, are expected to share more commonalities than any random collection of text snippets. Furthermore, we observe ... See full document
10
Automatic and Human Scoring of Word Definition Responses
... are short (1 – 10 words) our task falls somewhere between word-word simi- larity and passage ...the semantic similar- ity of individual ...of text semantic similarity work, by (Mihalcea ... See full document
8
Get Semantic With Me! The Usefulness of Different Feature Types for Short Answer Grading
... the semantic and TE features (CSSAG) or just the TE features (CREE) are individually predictive within four points F-score of the full model, but do not improve combined model ...(CREG: similarity and ... See full document
10
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling
... the similarity matrix obtained from prod- ucts of query and document embeddings and build sophisticated modules on top to capture addi- tional n-gram matching and term importance sig- ...more semantic ... See full document
12
Identifying Patterns For Short Answer Scoring Using Graph based Lexico Semantic Text Matching
... An answer containing the text diet of koalas would be coded as follows: ...the text should start at the beginning, and (2) the choice operator (|), which captures alternate ways of expressing the ... See full document
10
Inject Rubrics into Short Answer Grading System
... and similarity measures between student answers and reference ...Latent Semantic Analysis (LSA) (Mohler et ...distance-based similarity, and knowledge- based similarity using WordNet (Pedersen ... See full document
8
Summarization Evaluation meets Short Answer Grading
... an automatic summary, the standard evaluation method ROUGE (derived from Transla- tion evaluation’s BLEU) compares candidate sum- maries against manually created references (Lin, 2004), with the goal of comparing ... See full document
7
PERFORMANCE OF SEPARATED RANDOM USER SCHEDULING (SRUS) AND JOUNT USER SCHEDULING (JUS) IN THE LONG TERM EVOLUTION ADVANCED
... important text snippets extracted by the Information Extraction systems. These text snippets are used to generate coherent, informative multi- document summaries by using IE based multi document ... See full document
9
Semantic Relatedness from Automatically Generated Semantic Networks
... of semantic similarity and relatedness of terms is an important problem of lexical ...disambiguation, text summarization and information re- trieval (Budanitsky and Hirst, ...measuring ... See full document
5
Automatic Text Summarization using Features Extraction and Fuzzy Logic Scoring
... with Text summarization which is the process of automatically creating a shorter version of one or more text ...Essentially, text summarization techniques are classified as Extractive and ...Lexical ... See full document
7
Computing Semantic Text Similarity Using Rich Features
... used semantic net of English, and it is an effective tool to find synonyms of nouns, verbs, adjectives and ...for similarity and relatedness measures, since it organizes nouns and verbs into hierarchies of ... See full document
9
Bayesian Supervised Domain Adaptation for Short Text Similarity
... For a variety of short text similarity tasks, domain adaptation improves average performance across dif- ferent domains, tasks, and training set sizes. Our adaptive model is also by far the least ... See full document
10
Unsupervised Sparse Vector Densification for Short Text Similarity
... to test how well we can classify sentences into even- t types without any training. There are eight type- s of events: life, movement, conflict, contact, etc. We chose all the sentences that contain event infor- mation ... See full document
6
DKPro Similarity: An Open Source Framework for Text Similarity
... These text similarity measures project texts onto high-dimensional vec- tors which are then ...Explicit Semantic Analysis (Gabrilovich and Markovitch, 2007) constructs the vector space on corpora ... See full document
6
Application of Natural Language Processing and Evidential Analysis to Web-Based Intelligence Information Acquisition
... In order to compare two pieces of textual information, we first applied the Hirst & Mohammad [14] method to calculate semantic similarities between the concepts in the texts. Roget’s thesaurus was chosen as a ... See full document
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