In this paper, we define the notion of a preventative expression and discuss a corpus study of such expressions in instructional text. We discuss our coding schema, which takes into account both form and function features, and present measures of inter-coder reliability for those features. We then discuss the correlations that exist between the function and the form features.
In computational linguistics, on the other hand, positive imperatives have been extensively investigated, both from the point of view of interpretation @cite_13 @cite_8 @cite_6 @cite_1 and generation @cite_9 @cite_10 @cite_4 @cite_7 . Little work, however, has been directed at negative imperatives. (for exceptions see the work of in interpretation and of in generation).
@cite_4 @cite_7 @cite_8 @cite_10 @cite_9 @cite_1 @cite_6 @cite_13
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We present a new approach to adding state and state-changing commands to a term language. As a formal semantics it can be seen as a generalization of predicate transformer semantics, but beyond that it brings additional opportunities for specifying and verifying programs. It is based on a construct called a phrase, which is a term of the form C r t, where C stands for a command and t stands for a term of any type. If R is boolean, C r R is closely related to the weakest precondition wp(C,R). The new theory draws together functional and imperative programming in a simple way. In particular, imperative procedures and functions are seen to be governed by the same laws as classical functions. We get new techniques for reasoning about programs, including the ability to dispense with logical variables and their attendant complexities. The theory covers both programming and specification languages, and supports unbounded demonic and angelic nondeterminacy in both commands and terms. Horn clause logic programming can be extended to include abduction with integrity constraints. In the resulting extension of logic programming, negation by failure can be simulated by making negative conditions abducible and by imposing appropriate denials and disjunctions as integrity constraints. This gives an alternative semantics for negation by failure, which generalises the stable model semantics of negation by failure. The abductive extension of logic programming extends negation by failure in three ways: (1) computation can be perfonned in alternative minimal models, (2) positive as well as negative conditions can be made abducible, and (3) other integrity constraints can also be accommodated. * This paper was written while the first author was at Imperial College. 235 Introduction The tenn "abduction" was introduced by the philosopher Charles Peirce [1931] to refer to a particular kind of hypothetical reasoning. In the simplest case, it has the fonn: From A and A fB infer B as a possible "explanation" of A. Abduction has been given prominence in Charniak and McDennot's [1985] "Introduction to Artificial Intelligence", where it has been applied to expert systems and story comprehension. Independently, several authors have developed deductive techniques to drive the generation of abductive hypotheses. Cox and Pietrzykowski [1986] construct hypotheses from the "dead ends" of linear resolution proofs. Finger and Genesereth [1985] generate "deductive solutions to design problems" using the "residue" left behind in resolution proofs. Poole, Goebel and Aleliunas [1987] also use linear resolution to generate hypotheses. All impose the restriction that hypotheses should be consistent with the "knowledge base". Abduction is a fonn of non-monotonic reasoning, because hypotheses which are consistent with one state of a knowledge base may become inconSistent when new knowledge is added. Poole [1988] argues that abduction is preferable to noh-monotonic logics for default reasoning. In this view, defaults are hypotheses fonnulated within classical logic rather than conclusions derived withln some fonn of non-monotonic logic. The similarity between abduction and default reasoning was also pointed out in [Kowalski, 1979]. In this paper we show how abduction can be integrated with logic programming, and we concentrate on the use of abduction to generalise negation by failure. Conditional Answers Compared with Abduction In the simplest case, a logic program consists of a set of Horn Clauses, which are used backward to_reduce goals to sub goals. The initial goal is solved when there are no subgollls left; In this paper we are concerned with the analysis of normative conflicts, or the detection of conflicting obligations, permissions and prohibitions in normative texts written in a Controlled Natural Language (CNL). For this we present AnaCon, a proof-of-concept system where normative texts written in CNL are automatically translated into the formal language CL using the Grammatical Framework (GF). Such CL expressions are then analysed for normative conflicts by the CLAN tool, which gives counter-examples in cases where conflicts are found. The framework also uses GF to give a CNL version of the counter-example, helping the user to identify the conflicts in the original text. We detail the application of AnaCon to two case studies and discuss the effectiveness of our approach. Instructional texts have been the object of many studies recently, motivated by the increased need to produce manuals (especially multilingual manuals) coupled with the cost of translators and technical writers. Because these studies concentrate on aspects other than the linguistic realisation of instructions -- for example, the integration of text and graphics - they all generate a sequence of steps required to achieve a task, using imperatives. Our research so far shows, however, that manuals can in fact have different styles, i. e., not all instructions are stated using a sequence of imperatives, and that, furthermore, different parts of manuals often use different styles. In this paper, we present our preliminary results from an analysis of over 30 user guides manuals for consumer appliances and discuss some of the implications. This paper puts forward a normative framework for computational societies which enables the handling of incomplete knowledge about normative relations. In particular, attempts to perform a social action are evaluated as permitted, prohibited (i.e. not permitted) or pending for execution (i.e. neither permitted nor prohibited). This latter category of attempts can eventually be resolved as permitted or prohibited attempts using the speech acts allow and forbid. We make use of the support for incompleteness of action language K in the formalisation of the framework. The proposal will be illustrated with some scenarios drawn from the management of university courses. Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails, contradicts, or is logically neutral with respect to. We show that, in a significant portion of such data, this protocol leaves clues that make it possible to identify the label by looking only at the hypothesis, without observing the premise. Specifically, we show that a simple text categorization model can correctly classify the hypothesis alone in about 67 of SNLI (Bowman et. al, 2015) and 53 of MultiNLI (Williams et. al, 2017). Our analysis reveals that specific linguistic phenomena such as negation and vagueness are highly correlated with certain inference classes. Our findings suggest that the success of natural language inference models to date has been overestimated, and that the task remains a hard open problem. This paper proposes a data-driven method for concept-to-text generation, the task of automatically producing textual output from non-linguistic input. A key insight in our approach is to reduce the tasks of content selection ("what to say") and surface realization ("how to say") into a common parsing problem. We define a probabilistic context-free grammar that describes the structure of the input (a corpus of database records and text describing some of them) and represent it compactly as a weighted hypergraph. The hypergraph structure encodes exponentially many derivations, which we rerank discriminatively using local and global features. We propose a novel decoding algorithm for finding the best scoring derivation and generating in this setting. Experimental evaluation on the Atis domain shows that our model outperforms a competitive discriminative system both using BLEU and in a judgment elicitation study. Human agents are extremely flexible in dealing with Natural Language instructions. I argue that most instructions don't exactly mirror the agent's knowledge, but are understood by accommodating them in the context of the general plan the agent is considering; the accommodation process is guided by the goal(s) that the agent is trying to achieve. Therefore a NL system which interprets instructions must be able to recognize and or hypothesize goals; it must make use of a flexible knowledge representation system, able to support the specialized inferences necessary to deal with input action descriptions that do not exactly match the stored knowledge. The data that support my claim are Purpose Clauses (PCs), infinitival constructions as in @math , and Negative Imperatives. I present a pragmatic analysis of both PCs and Negative Imperatives. Furthermore, I analyze the computational consequences of PCs, in terms of the relations between actions PCs express, and of the inferences an agent has to perform to understand PCs. I propose an action representation formalism that provides the required flexibility. It has two components. The Terminological Box (TBox) encodes linguistic knowledge about actions, and is expressed by means of the hybrid system CLASSIC. To guarantee that the primitives of the representation are linguistically motivated, I derive them from Jackendoff's work on Conceptual Structures. The Action Library encodes planning knowledge about actions. The action terms used in the plans are those defined in the TBox. Finally, I present an algorithm that implements inferences necessary to understand @math , and supported by the formalism I propose. In particular, I show how the TBox classifier is used to infer whether @math can be assumed to match one of the substeps in the plan for @math , and how expectations necessary for the match to hold are computed. This paper argues that the judicial use of formal language theory and grammatical inference are invaluable tools in understanding how deep neural networks can and cannot represent and learn long-term dependencies in temporal sequences. Learning experiments were conducted with two types of Recurrent Neural Networks (RNNs) on six formal languages drawn from the Strictly Local (SL) and Strictly Piecewise (SP) classes. The networks were Simple RNNs (s-RNNs) and Long Short-Term Memory RNNs (LSTMs) of varying sizes. The SL and SP classes are among the simplest in a mathematically well-understood hierarchy of subregular classes. They encode local and long-term dependencies, respectively. The grammatical inference algorithm Regular Positive and Negative Inference (RPNI) provided a baseline. According to earlier research, the LSTM architecture should be capable of learning long-term dependencies and should outperform s-RNNs. The results of these experiments challenge this narrative. First, the LSTMs' performance was generally worse in the SP experiments than in the SL ones. Second, the s-RNNs out-performed the LSTMs on the most complex SP experiment and performed comparably to them on the others. Many of the creative and figurative elements that make language exciting are lost in translation in current natural language generation engines. In this paper, we explore a method to harvest templates from positive and negative reviews in the restaurant domain, with the goal of vastly expanding the types of stylistic variation available to the natural language generator. We learn hyperbolic adjective patterns that are representative of the strongly-valenced expressive language commonly used in either positive or negative reviews. We then identify and delexicalize entities, and use heuristics to extract generation templates from review sentences. We evaluate the learned templates against more traditional review templates, using subjective measures of "convincingness", "interestingness", and "naturalness". Our results show that the learned templates score highly on these measures. Finally, we analyze the linguistic categories that characterize the learned positive and negative templates. We plan to use the learned templates to improve the conversational style of dialogue systems in the restaurant domain. Binary classifiers are employed as discriminators in GAN-based unsupervised style transfer models to ensure that transferred sentences are similar to sentences in the target domain. One difficulty with the binary discriminator is that error signal is sometimes insufficient to train the model to produce rich-structured language. In this paper, we propose a technique of using a target domain language model as the discriminator to provide richer, token-level feedback during the learning process. Because our language model scores sentences directly using a product of locally normalized probabilities, it offers more stable and more useful training signal to the generator. We train the generator to minimize the negative log likelihood (NLL) of generated sentences evaluated by a language model. By using continuous approximation of the discrete samples, our model can be trained using back-propagation in an end-to-end way. Moreover, we find empirically with a language model as a structured discriminator, it is possible to eliminate the adversarial training steps using negative samples, thus making training more stable. We compare our model with previous work using convolutional neural networks (CNNs) as discriminators and show our model outperforms them significantly in three tasks including word substitution decipherment, sentiment modification and related language translation. We identify and validate from a large corpus constraints from conjunctions on the positive or negative semantic orientation of the conjoined adjectives. A log-linear regression model uses these constraints to predict whether conjoined adjectives are of same or different orientations, achieving 82 accuracy in this task when each conjunction is considered independently. Combining the constraints across many adjectives, a clustering algorithm separates the adjectives into groups of different orientations, and finally, adjectives are labeled positive or negative. Evaluations on real data and simulation experiments indicate high levels of performance: classification precision is more than 90 for adjectives that occur in a modest number of conjunctions in the corpus. ABSTRACT This paper concerns the semantics of Codd's relational model of data. Formulated are precise conditions that should be satisfied in a semantically meaningful extension of the usual relational operators, such as projection, selection, union, and join, from operators on relations to operators on tables with “null values” of various kinds allowed. These conditions require that the system be safe in the sense that no incorrect conclusion is derivable by using a specified subset Ω of the relational operators; and that it be complete in the sense that all valid conclusions expressible by relational expressions using operators in Ω are in fact derivable in this system. Two such systems of practical interest are shown. The first, based on the usual Codd's null values, supports projection and selection. The second, based on many different (“marked”) null values or variables allowed to appear in a table, is shown to correctly support projection, positive selection (with no negation occurring in the selection condition), union, and renaming of attributes, which allows for processing arbitrary conjunctive queries. A very desirable property enjoyed by this system is that all relational operators on tables are performed in exactly the same way as in the case of the usual relations. A third system, mainly of theoretical interest, supporting projection, selection, union, join, and renaming, is also discussed. Under a so-called closed world assumption, it can also handle the operator of difference. It is based on a device called a conditional table and is crucial to the proof of the correctness of the second system. All systems considered allow for relational expressions containing arbitrarily many different relation symbols, and no form of the universal relation assumption is required. Categories and Subject Descriptors: H.2.3 [Database Management]: Languages— query languages; H.2.4 [Database Management]: Systems— query processing General Terms: Theory This article explores a combination of deep and shallow approaches to the problem of resolving the scope of speculation and negation within a sentence, specifically in the domain of biomedical research literature. The first part of the article focuses on speculation. After first showing how speculation cues can be accurately identified using a very simple classifier informed only by local lexical context, we go on to explore two different syntactic approaches to resolving the in-sentence scopes of these cues. Whereas one uses manually crafted rules operating over dependency structures, the other automatically learns a discriminative ranking function over nodes in constituent trees. We provide an in-depth error analysis and discussion of various linguistic properties characterizing the problem, and show that although both approaches perform well in isolation, even better results can be obtained by combining them, yielding the best published results to date on the CoNLL-2010 Shared Task data. The last part of the article describes how our speculation system is ported to also resolve the scope of negation. With only modest modifications to the initial design, the system obtains state-of-the-art results on this task also. Concept-to-text generation refers to the task of automatically producing textual output from non-linguistic input. We present a joint model that captures content selection ("what to say") and surface realization ("how to say") in an unsupervised domain-independent fashion. Rather than breaking up the generation process into a sequence of local decisions, we define a probabilistic context-free grammar that globally describes the inherent structure of the input (a corpus of database records and text describing some of them). We recast generation as the task of finding the best derivation tree for a set of database records and describe an algorithm for decoding in this framework that allows to intersect the grammar with additional information capturing fluency and syntactic well-formedness constraints. Experimental evaluation on several domains achieves results competitive with state-of-the-art systems that use domain specific constraints, explicit feature engineering or labeled data. Abstract Theories of lexical representation in production provide sophisticated accounts of the way in which information is activated during lexical access (e.g., Levelt, Roelofs, & Meyer, 1999 ), but there has been little attempt to account for the way in which the structure of the lexical entry affects the formulation processes that underlie the production of complex expressions. This paper first outlines such an account, and then reports three experiments that investigated the priming of noun-phrase structure in dialogue. Experiment 1 showed that speakers used a complex noun phrase containing a relative clause (e.g., “the square that’s red”) more often after hearing a syntactically similar noun phrase than after hearing a simple noun phrase, and that this effect was enhanced when the head noun (“square”) was repeated. Experiment 2 showed an enhanced priming effect when prime and target contained semantically related nouns (e.g., “goat” and “sheep”). Experiment 3 showed no enhanced effect when prime and target bore a close phonological relationship (e.g., “ship” and “sheep”). These results provide support for our account, and suggest that syntactic encoding may be unaffected by phonological feedback. Abstract In recent years, two conceptually different interpretations of default expressions have been advanced: extensional interpretations, in which defaults are regarded as prescriptions for extending one's set of beliefs, and conditional interpretations, in which defaults are regarded as beliefs whose validity is bound to a particular context. The two interpretations possess virtues and limitations that are practically orthogonal to each other. The conditional interpretations successfully resolve arguments of different “specificity” (e.g., “penguins don't fly in spite of being birds”) but fail to capture arguments of “irrelevance” (e.g., concluding “ red birds fly” from “birds fly”). The opposite is true for the extensional interpretations. This paper develops a new account of defaults, called conditional entailment , which combines the benefits of the two interpretations. Like prioritized circumscriptions, conditional entailment resolves arguments by enforcing priorities among defaults. However, instead of having to be specified by the user, these priorities are extracted automatically from the knowledge base. Similarly, conditional entailment possesses a sound and complete proof theory, based on interacting arguments and amenable to implementation in conventional ATMSs. This paper develops techniques for studying complexity classes that are not covered by known recursive enumerations of machines. Often, counting classes, probabilistic classes, and intersection classes lack such enumerations. Concentrating on the counting class UP, we show that there are relativizations for which UPA has no complete languages and other relativizations for which PB ≠ UPB ≠ NPB and UPB has complete languages. Among other results we show that P ≠ UP if and only if there exists a set S in P of Boolean formulas with at most one satisfying assignment such that S ∩ SAT is not in P. P ≠ UP ∩ coUP if and only if there exists a set S in P of uniquely satisfiable Boolean formulas such that no polynomial-time machine can compute the solutions for the formulas in S. If UP has complete languages then there exists a set R in P of Boolean formulas with at most one satisfying assignment so that SAT ∩ R is complete for UP. Finally, we indicate the wide applicability of our techniques to counting and probabilistic classes by using them to examine the probabilistic class BPP. There is a relativized world where BPPA has no complete languages. If BPP has complete languages then it has a complete language of the form B ∩ MAJORITY, where B ∈ P and MAJORITY = f | f is true for at least half of all assignments is the canonical PP-complete set. Objective: Negation is common in clinical documents and is an important source of poor precision in automated indexing systems. Previous research has shown that negated terms may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We describe a novel hybrid approach, combining regular expression matching with grammatical parsing, to address the above limitation in automatically detecting negations in clinical radiology reports. Design: Negations are classified based upon the syntactical categories of negation signals, and negation patterns, using regular expression matching. Negated terms are then located in parse trees using corresponding negation grammar. Measurements: A classification of negations and their corresponding syntactical and lexical patterns were developed through manual inspection of 30 radiology reports and validated on a set of 470 radiology reports. Another 120 radiology reports were randomly selected as the test set on which a modified Delphi design was used by four physicians to construct the gold standard. Results: In the test set of 120 reports, there were a total of 2,976 noun phrases, of which 287 were correctly identified as negated (true positives), along with 23 undetected true negations (false negatives) and 4 mistaken negations (false positives). The hybrid approach identified negated phrases with sensitivity of 92.6 (95 CI 90.9- 93.4 ), positive predictive value of 98.6 (95 CI 96.9-99.4 ), and specificity of 99.87 (95 CI 99.7-99.9 ). Conclusion: This novel hybrid approach can accurately locate negated concepts in clinical radiology reports not only when in close proximity to, but also at a distance from, negation signals. The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8p on the full test set, but the accuracy rises above 95p when the algorithm is allowed to abstain from classifying mild words.