This study presents an evaluation of WordNet-based semantic similarity and relatedness measures in tasks focused on concept similarity. Assuming similarity as distinct from relatedness, the goal is to fill a gap within the current body of work in the evaluation of similarity and relatedness measures. Past studies have either focused entirely on relatedness or only evaluated judgments over words rather than concepts. In this study, first, concept similarity measures are evaluated over human judgments by using existing sets of word similarity pairs that we annotated with word senses. Next, an application-oriented study is presented by integrating similarity and relatedness measures into an algorithm which relies on concept similarity. Interestingly, the results find metrics categorized as measuring relatedness to be strongest in correlation with human judgments of concept similarity, though the difference in correlation is small. On the other hand, an information content metric, categorized as measuring similarity, is notably strongest according to the application-oriented evaluation.

Paraphrase identification is a core Natural Language Processing task that involves assessing the semantic similarity of two texts. To foster systematic studies of this task, standardized datasets were created on which various approaches could be compared more fairly. However, a better understanding and more precise operational definition of a paraphrase are needed before any further datasets or systematic evaluations of the task of paraphrase identification are proposed. This study develops the concept of paraphrasing as a writing strategy. Six types of paraphrases are defined through the creation of a relatively large corpus of student-generated paraphrases. These paraphrases are analyzed along several dozen linguistic dimensions ranging from cohesion to lexical diversity. The most significant indices from these dimensions were then used to build a prediction model that could identify true and false paraphrases and each of the six paraphrase types.

Using the Gramulator, we analyzed the linguistic features of ESL texts and fairy tales. Our goal was to determine if fairy tales had the potential to be used as reading material for English language learners. The results of our analyses suggest that there are significant similarities between fairy tales and ESL texts, but that differences lie in the content of the text types with fairy tales appearing significantly more narrative in style and ESL texts appearing more expository.

Natural language processing and statistical methods were used to identify linguistic features associated with the quality of student-generated paragraphs. Linguistic features were assessed using Coh-Metrix. The resulting computational models demonstrated small to medium effect sizes for predicting paragraph quality: introduction quality r2 = .25, body quality r2 = .10, and conclusion quality r2 = .11. Although the variance explained was somewhat low, the linguistic features identified were consistent with the rhetorical goals of paragraph types. Avenues for bolstering this approach by considering individual writing styles and techniques are considered.

Dialogue-based intelligent tutoring systems use speech act classifiers to categorize student input into answers, questions, and other speech acts. Previous work has primarily focused on question classification. In this paper, we present a complimentary speech act classifier that focuses primarily on non-questions, which was developed using machine learning techniques. Our results show that an effective speech act classifier can be developed directly from labeled data using decision trees.

This paper considers a scenario when we are given almost perfect knowledge about bilingual terminology in terms of a test corpus in Statistical Machine Translation (SMT). When the given terminology is part of a training corpus, one natural strategy in SMT is to use the trained translation model ignoring the given terminology. Then, two questions arises here. 1) Can a word aligner capture the given terminology? This is since even if the terminology is in a training corpus, it is often the case that a resulted translation model may not include these terminology. 2) Are probabilities in a translation model correctly calculated? In order to answer these questions, we did experiment introducing a Multi-Word Expression-sensitive (MWE-sensitive) word aligner and a hierarchical Pitman-Yor process-based translation model smoothing. Using 200k JP--EN NTCIR corpus, our experimental results show that if we introduce an MWE-sensitive word aligner and a new translation model smoothing, the overall improvement was 1.35 BLEU point absolute and 6.0% relative compared to the case we do not introduce these two.

We present in this paper a novel solution to the problem of paraphrase identification based on lexical dissimilarity kernels. Lexical kernels in conjunction with Support Vector Machines are preferred over other learning methods, e.g. decision trees, due to their ability to handle a high number of features. Dissimilarity-based kernels emphasize dissimilarities among text fragments and therefore are appropriate for text similarity tasks characterized by high lexical overlap. We conducted experiments with our kernels on the Microsoft Research (MSR) Paraphrase Corpus, a standardized data set used for assessing approaches to paraphrase identification. Our reported accuracy results are competitive and robust when compared to state-of-the-art single-model approaches. The results were obtained using 10-fold cross-validation over the entire corpus. We also report competitive results on the test portion of the MSR Paraphrase Corpus, which is the standard way to report results on this corpus.

Closely related genres have complex interrelations. An antecedent genre can constrain a subsequent genre, but changing rhetorical situations can lead to distinctions between an antecedent and its descendent. In this study, we assess two genres of detective fiction to determine their hierarchical relation to one another. We use the Gramulator, a computational tool that identifies indicative lexical features, to explain the relationship between whodunit fiction and hardboiled fiction . We conclude, based on the indicative lexical features of the expositions in texts, that the two are sibling genres.

In this paper we introduce a system that is designed to automatically populate a knowledge base from both structured and unstructured text given an ontology. Our system is designed as a modular end-to-end system that takes structured or unstructured data as input, extracts information, maps relevant information to an ontology, and finally disambiguates entities in the knowledge base. The novelty of our approach is that it is domain independent and can easily be adapted to new ontologies and domains. Unlike most knowledge base population systems, ours includes entity detection. This feature allows one to employ very complex ontologies that include events and the entities that are involved in the events.

However, word concreteness is not an attribute that a A single word in the human language has many complex computer can directly compute. One means of assessing dimensions such as semantics, parts of speech, lexical type, the characteristics of words is by having humans rate them imagability, concreteness, familiarity, etc. It is important to on the dimensions of interest. Humans are proficient in know the dimensions of words in languages so that we can categorizing words into linguistic dimensions, but it is develop a better theoretical understanding of language and impractical to have humans rating tens of thousands of also to build tools that simulate human intelligence and words that we would need for psycholinguistic research.