Counterfactual conditional sentences can be useful in artificial intelligence as they are in human affairs. In particular, they allow reasoners to learn from experiences that they did not quite have. Our tools for making inferences from counterfactuals permit inferring sentences that are not themselves counterfactual. This is what makes them useful. A simple class of useful counterfactuals invo...

OBJECTIVE: The prior knowledge about the rhetorical structure of scientific abstracts is useful for various text-mining tasks such as information extraction, information retrieval, and automatic summarization. This paper presents a novel approach to categorize sentences in scientific abstracts into four sections, objective, methods, results, and conclusions. METHOD: Formalizing the categorizati...

Neural machine translation decoders are usually conditional language models to sequentially generate words for target sentences. This approach is limited to find the best word composition and requires help of explicit methods as beam search. To help learning correct compositional mechanisms in NMTs, we propose concept equalization using direct mapping distributed representations of source and t...

We study a novel shallow information extraction problem that involves extracting sentences of a given set of topic categories from medical forum data. Given a corpus of medical forum documents, our goal is to extract two related types of sentences that describe a biomedical case (i.e., medical problem descriptions and medical treatment descriptions). Such an extraction task directly generates m...

Abstract According to the PCCP thesis, probability of a conditional A ? C is P( | ). This claim undermined by Lewis’ triviality results, which purport show that apart from trivial cases, not true. In present article we only rational, “Dutch Book-resistant” extension agent’s beliefs concerning non-conditional sentences and assuming ) = (i.e., in accord with PCCP). other cases diachronic Dutch Bo...

OBJECTIVE A system that translates narrative text in the medical domain into structured representation is in great demand. The system performs three sub-tasks: concept extraction, assertion classification, and relation identification. DESIGN The overall system consists of five steps: (1) pre-processing sentences, (2) marking noun phrases (NPs) and adjective phrases (APs), (3) extracting conce...

Conditional sentences with quantifying expressions are systematically ambigous. In one reading, the if -clause restricts the domain of the overt quantifier; in the other, the if -clause restricts the domain of a covert quantifier, which defaults to epistemic necessity. Although the ambiguity follows directly from the LewisKratzer line on if, it is not generally acknowledged, which has led to ps...

What makes a proposition true or false has traditionally played an essential role in philosophical and linguistic theories of meaning. A comprehensive neurobiological theory of language must ultimately be able to explain the combined contributions of real-world truth-value and discourse context to sentence meaning. This fMRI study investigated the neural circuits that are sensitive to the propo...

The paper presents our work in the opinion pilot task in NTCIR6 in Chinese. In extracting opinion holders, we applied Conditional Random Field (CRF) model to find the opinion holders as a sequential labeling task, while in determining the subjectivity and the polarity, we adopted a simple empirical algorithms based on the sentimental dictionary to discriminate the subjective sentences from the ...

This paper presents a method for inducing a context-sensitive conditional probability context-free grammar from an unlabeled bracketed corpus using local contextual information and describes a natural language parsing model which uses a probabilitybased scoring function of the grammar to rank parses of a sentence. This method uses clustering techniques to group brackets in a corpus into a numbe...