We introduce NPIC, an image classification system that focuses on synthetic (e.g., non-photographic) images. We use class-specific keywords in an image search engine to create a noisily labeled training corpus of images for each class. NPIC then extracts both content-based image retrieval (CBIR) features and metadata-based textual features for each image for machine learning. We evaluate this a...

Multimedia content can be described in versatile ways as its essence is not limited to one view. For music data these multiple views could be a song’s audio features as well as its lyrics. Both of these modalities have their advantages as text may be easier to search in and could cover more of the ‘content semantics’ of a song, while omitting other types of semantic categorisation. (Psycho)acou...

The main goal of FBK-irst participation at RTE-4 was to experiment the use of combined specialized entailment engines, each addressing a specific phenomena relevant to entailment. The approach is motivated since textual entailment is due to the combination of several linguistic phenomena which interact among them in a quite complex way. We were driven by the following two considerations: (i) de...

This paper presents a methodology for a quantitative and qualitative evaluation of Textual Entailment systems. We take advantage of the decomposition of Text Hypothesis pairs into monothematic pairs, i.e. pairs where only one linguistic phenomenon at a time is responsible for entailment judgment, and propose to run TE systems over such datasets. We show that several behaviours of a system can b...

Hypernymy, textual entailment, and image captioning can be seen as special cases of a single visual-semantic hierarchy over words, sentences, and images. In this paper we advocate for explicitly modeling the partial order structure of this hierarchy. Towards this goal, we introduce a general method for learning ordered representations, and show how it can be applied to a variety of tasks involv...

We present a novel text exploration model, which extends the scope of state-of-the-art technologies by moving from standard concept-based exploration to statement-based exploration. The proposed scheme utilizes the textual entailment relation between statements as the basis of the exploration process. A user of our system can explore the result space of a query by drilling down/up from one stat...

The Stanford Dependencies are a deep syntactic representation that are widely used for semantic tasks, like Recognizing Textual Entailment. But do they capture all of the semantic information a meaning representation ought to convey? This paper explores this question by investigating the feasibility of mapping Stanford dependency parses to Hobbsian Logical Form, a practical, event-theoretic sem...

This paper describes our system of recognizing textual entailment for RITE Traditional and Simplified Chinese subtasks at NTCIR10. We build a textual entailment recognition framework and implement a system that employs features of three categories, including string, structure and linguistic features, for the recognition. In addition, an entailment transformation approach is leveraged to align t...

Prosodic phrase boundaries (PBs) are a key aspect of spoken communication. In automatic PB detection, it is common to use local acoustic features, textual features, or a combination of both. Most approaches – regardless of features used – succeed in detecting major PBs (break score “4” in ToBI annotation, typically involving a pause) while detection of intermediate PBs (break score “3” in ToBI ...

We present a Textual Entailment (TE) recognition system that uses semantic features based on the Universal Networking Language (UNL). The proposed TE system compares the UNL relations in both the text and the hypothesis to arrive at the two-way entailment decision. The system has been separately trained on each development corpus released as part of the Recognizing Textual Entailment (RTE) comp...