An important problem in human-robot interaction is that of referring expressions: phrases used to identify a particular object among others. Existing parsing models all operate on entire sentences. Incrementally parsing referring expressions is important for human-robot interaction and conversational feedback. We present a model for parsing real-word referring expressions, trained and tested on...

Temporal expressions are important structures in natural language. In order to understand text, temporal expressions have to be extracted and normalized. In this paper we present and compare two approaches for the automatic recognition of temporal expressions, based on a supervised machine learning approach and trained on TimeBank. The first approach performs a tokenby-token classification and ...

Corpora of referring expressions paired with their visual referents are a good source for learning word meanings directly grounded in visual representations. Here, we explore additional ways of extracting from them word representations linked to multi-modal context: through expressions that refer to the same object, and through expressions that refer to different objects in the same scene. We s...

The purpose of this paper is to discuss the categorial-denotational status of before and after phrases, trying to assess whether they are better classified as temporal locating expressions, timedenoting expressions or both. The conclusions to be drawn possibly apply to the counterparts of these expressions in other languages as well. I will concentrate on the occurrences of before and after in ...

While traditional information extraction systems have been built to answer questions about facts, subjective information extraction systems will answer questions about feelings and opinions. A crucial step towards this goal is identifying the words and phrases that express opinions in text. Indeed, although much previous work has relied on the identification of opinion expressions for a variety...

Temporal expressions are important structures in natural language. In order to understand text, temporal expressions have to be extracted and normalized to ISO-based values. For these purposes rule-based and machine learning techniques were proposed. In this paper we present and compare two approaches for automatic recognition of temporal expressions in free text, based on a supervised machine ...

We proposed a method of collecting humorous expressions from an online community-based question-answering (CQA) corpus where some users post a variety of questions and other users post relevant answers. Although the service is created for the purpose of knowledge exchange, there are users who enjoy posting humorous responses. Therefore, the corpus contains many interesting humour communication ...

A method for verbal expression of degree of uncertainty is described. It requires the subject to select a phrase from a list that spans the full range of probabilities. In a second, optional, step, the subject indicates the numerical meaning of each phrase. The method avoids two problems of verbal probabilities-the indefinitely large lexicon and the individual differences in the interpretation ...

We present a probabilistic approach for learning to interpret temporal phrases given only a corpus of utterances and the times they reference. While most approaches to the task have used regular expressions and similar linear pattern interpretation rules, the possibility of phrasal embedding and modification in time expressions motivates our use of a compositional grammar of time expressions. T...