Conditional sentences are difficult to analyse and the literature on this topic is so abundant that I do not dare to mention any title except for those mentioned for the specific purposes of this paper. The difficulty starts already at classificational and typological levels. The purpose of this paper is to analyse conditional sentences (CSs, for short) in which specific items, so-called catego...

We identify a blind spot in the early Theory of Mind processing of conditional sentences that describe a protagonist's potential action, and its predictable consequences. We propose that such sentences create expectations through two independent channels. A decision theoretic channel creates an expectation that the action will be taken (viz., not taken) if it has desirable (viz., undesirable) c...

This paper presents a CRF (Conditional Random Field) model for Semantic Chunk Annotation in a Chinese Question and Answering System (SCACQA). The model was derived from a corpus of real world questions, which are collected from some discussion groups on the Internet. The questions are supposed to be answered by other people, so some of the questions are very complex. Mutual information was adop...

In this paper, we propose a novel idea for applying probabilistic graphical models for automatic text summarization task related to a legal domain. Identification of rhetorical roles present in the sentences of a legal document is the important text mining process involved in this task. A Conditional Random Field (CRF) is applied to segment a given legal document into seven labeled components a...

Online forums are becoming a popular resource in the state of the art question answering (QA) systems. Because of its nature as an online community, it contains more updated knowledge than other places. However, going through tedious and redundant posts to look for answers could be very time consuming. Most prior work focused on extracting only question answering sentences from user conversatio...

Academic publishers' full text databases are an important part of the deep Web for researchers and a potentially valuable resource for automated extraction of scientific knowledge. Recently, some major publishers have provided Web APIs for accessing their article databases, thus allowing the development of Web applications to mine these resources. However the task of knowledge discovery from ac...

This paper proposes a novel context-aware method for analyzing sentiment at the level of individual sentences. Most existing machine learning approaches suffer from limitations in the modeling of complex linguistic structures across sentences and often fail to capture nonlocal contextual cues that are important for sentiment interpretation. In contrast, our approach allows structured modeling o...

We automatically extract the summaries of spoken class lectures. This paper presents a novel method for sentence extraction-based automatic speech summarization. We propose a technique that extracts “cue phrases for important sentences (CPs)” that often appear in important sentences. We formulate CP extraction as a labeling problem of word sequences and use Conditional Random Fields (CRF) [1] f...

Several authors propose that performative speech acts are self-guaranteeing due to their self-referential nature (Searle 1989; Jary 2007). The present paper offers an analysis of self-referentiality in terms of truth conditional semantics, making use of Davidsonian events. I propose that hereby can denote the ongoing act of information transfer (more mundanely, the utterance) which thereby ente...

Many methods, including supervised and unsupervised algorithms, have been developed for extractive document summarization. Most supervised methods consider the summarization task as a twoclass classification problem and classify each sentence individually without leveraging the relationship among sentences. The unsupervised methods use heuristic rules to select the most informative sentences in...