One of the main challenges in question-answering is the potential mismatch between the expressions in questions and the expressions in texts. While humans appear to use inference rules such as “X writes Y ” implies “X is the author of Y ” in answering questions, such rules are generally unavailable to question-answering systems due to the inherent difficulty in constructing them. In this paper, we present an unsupervised algorithm for discovering inference rules from text. Our algorithm is based on an extended version of Harris ’ Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus. Essentially, if two paths tend to link the same set of words, we hypothesize that their meanings are similar. We use examples to show that our system discovers many inference rules easily missed by humans. 1

While paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases. We present an unsupervised learning algorithm for identification of paraphrases from a corpus of multiple English translations of the same source text. Our approach yields phrasal and single word lexical paraphrases as well as syntactic paraphrases. 1

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We address the text-to-text generation problem of sentence-level paraphrasing --- a phenomenon distinct from and more difficult than word- or phrase-level paraphrasing. Our approach applies multiple-sequence alignment to sentences gathered from unannotated comparable corpora: it learns a set of paraphrasing patterns represented by word lattice pairs and automatically determines how to apply these patterns to rewrite new sentences. The results of our evaluation experiments show that the system derives accurate paraphrases, outperforming baseline systems.

In this paper, we propose an unsupervised method for discovering inference rules from text, such as "X is author of Y X wrote Y", "X solved Y X found a solution to Y", and "X caused Y Y is triggered by X". Inference rules are extremely important in many fields such as natural language processing, information retrieval, and artificial intelligence in general. Our algorithm is based on an extended version of Harris's Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus.

We present a method to automatically generate a concise summary by identifying and synthesizing similar elements across related text from a set of multiple documents. Our approach is unique in its usage of language generation to reformulate the wording of the summary.

So That Nobody Has To Go To School If They Don't Want To by Roger Sipher A decline in standardized test scores is but the most recent indicator that American education is in trouble. One reason for the crisis is that present mandatory-attendance laws force many to attend school who have no wish to be there. Such children have little desire to learn and are so antagonistic to school that neither they nor more highly motivated students receive the quality education that is the birthright of every American. The solution to this problem is simple: Abolish compulsory-attendance laws and allow only those who are committed to getting an education to attend. This will not end public education. Contrary to conventional belief, legislators enacted compulsory-attendance laws to legalize what already existed. William Landes and Lewis Solomon, economists, found little evidence that mandatory-attendance laws increased the number of children in school. They found, too, that school systems have never effectively enforced such laws, usually because of the expense involved. There is no contradiction between the assertion that compulsory attendance has had little effect on the number of children attending school and the argument that repeal would be a positive step toward improving education. Most parents want a high school education for their children. Unfortunately, compulsory attendance hampers the ability of public school officials to enforce legitimate educational and disciplinary policies and thereby make the education a good one. Private schools have no such problem. They can fail or dismiss students, knowing such students can attend public school. Without compulsory attendance, public schools would be freer to oust students whose academic or personal behavior undermines the educational mission of the institution. Has not the noble experiment of a formal education for everyone failed? While we pay homage to the homily, "You can lead a horse to water but you can't make him drink," we have pretended it is not true in education. Ask high school teachers if recalcitrant students learn anything of value. Ask teachers if these students do any homework. Quite the contrary, these students know they will be passed from grade to grade until they are old enough to quit or until, as is more likely, they receive a high school diploma. At the point when students could legally quit, most choose to remain since they know they are likely to be allowed to graduate whether they do acceptable work or not. Abolition of archaic attendance laws would produce enormous dividends. First, it would alert everyone that school is a serious place where one goes to learn. Schools are neither day-care centers nor indoor street corners. Young people who resist learning should stay away; indeed, an end to compulsory schooling would require them to stay away. Second, students opposed to learning would not be able to pollute the educational atmosphere for those who want to learn. Teachers could stop policing recalcitrant students and start educating. Third, grades would show what they are supposed to: how well a student is learning. Parents could again read report cards and know if their children were making progress. Fourth, public esteem for schools would increase. People would stop regarding them as way stations for adolescents and start thinking of them as institutions for educating America's youth. Fifth, elementary schools would change because students would find out early they had better learn something or risk flunking out later. Elementary teachers would no longer have to pass their failures on to junior high and high school. Sixth, the cost of enforcing compulsory education would be eliminated. Despite enforcement efforts, nearly 15 percent of the school-age children in our largest cities are almost permanently absent from school. Communities could use these savings to support institutions to deal with young people not in school. If, in the long run, these institutions prove more costly, at least we would not confuse their mission with that of schools. Schools should be for education. At present, they are only tangentially so. They have attempted to serve an all-encompassing social function, trying to be all things to all people. In the process they have failed miserably at what they were originally formed to accomplish.

This paper describes surge, a syntactic realization front-end for natural language generation systems. By gradually integrating complementary aspects of various linguistic theories within the computational framework of functional unification, surge has evolved to be one of the most comprehensive grammars of English for language generation available today. It has been successfully re-used in a variety of generators, with very different architectures and application domains. 1 Introduction This paper is an overview of surge (Systemic Unification Realization Grammar of English) a syntactic realization front-end for natural language generation systems. Developed over the last seven years 1 it embeds one of the most comprehensive computational grammar of English for generation available to date. It has been successfully re-used in eight generators, that have little in common in terms of architecture. It has also been used for teaching natural language generation at several academic inst...

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To explain complex phenomena, an explanation system must be able to select information from a formal representation of domain knowledge, organize the selected information into multisentential discourse plans, and realize the discourse plans in text. Although recent years have witnessed significant progress in the development of sophisticated computational mechanisms for explanation, empirical results have been limited. This paper reports on a seven-year effort to empirically study explanation generation from semantically rich, large-scale knowledge bases. In particular, it describes KNIGHT, a robust explanation system that constructs multisentential and multiparagraph explanations from the Biology Knowledge Base, a large-scale knowledge base in the domain of botanical anatomy, physiology, and development. We introduce the Two-Panel evaluation methodology and describe how KNIGHT'S performance was assessed with this methodology in the most extensive empirical evaluation conducted on an explanation system. In this evaluation, KNIGHT scored within "half a grade " of domain experts, and its performance exceeded that of one of the domain experts. 1.