We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Given an input sentence and a target word and frame, the system labels constituents with either abstract semantic roles such as AGENT or PATIENT, or more domain-specific semantic roles such as SPEAKER, MESSAGE, and TOPIC. The system is based on statist...

We describe a method for training a semantic role labeler for CCG in the absence of gold-standard syntax derivations. Traditionally, semantic role labeling is performed by placing human-annotated semantic roles on gold-standard syntactic parses, identifying patterns in the syntaxsemantics relationship, and then predicting roles on novel syntactic analyses. The gold standard syntactic training d...

Most research on semantic role labeling (SRL) has been focused on training and evaluating on the same corpus in order to develop the technology. This strategy, while appropriate for initiating research, can lead to over-training to the particular corpus. The work presented in this paper focuses on analyzing the robustness of an SRL system when trained on one genre of data and used to label a di...

Parsing plays an important role in semantic role labeling (SRL) because most SRL systems infer semantic relations from 1-best parses. Therefore, parsing errors inevitably lead to labeling mistakes. To alleviate this problem, we propose to use packed forest, which compactly encodes all parses for a sentence. We design an algorithm to exploit exponentially many parses to learn semantic relations ...

Most corpus-based approaches to language learning have focused on tasks for which a sufficient amount of human-labeled training data is available. However, it is difficult to produce such data, and models trained from such data tend to be brittle when applied to domains that vary, even in seemingly minor ways, from the training data. We claim that these difficulties can by overcome by applying ...

2. Segmentation and Classification: To do segmentation, the video stream is first divided into relatively static parts and active parts. For static parts, motion features are ignored and key frames are saved. Every active part is further segmented into active sub-parts according to 4 view types (defined in Section 2). In Classification stage, motion features are used to classify (label) segment...

We propose a new, simple model for the automatic induction of selectional preferences, using corpus-based semantic similarity metrics. Focusing on the task of semantic role labeling, we compute selectional preferences for semantic roles. In evaluations the similarity-based model shows lower error rates than both Resnik’s WordNet-based model and the EM-based clustering model, but has coverage pr...

Semantic applications typically extract information from intermediate structures derived from sentences, such as dependency parse or semantic role labeling. In this paper, we study Open Information Extraction’s (Open IE) output as an additional intermediate structure and find that for tasks such as text comprehension, word similarity and word analogy it can be very effective. Specifically, for ...

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We present a semantic parsing system to decompose a sentence into semantic-expressions/concepts for ESWC’14 semantic analysis challenge. The proposed system has a pipeline architecture, and is based on syntactic parsing and semantic role labeling of the candidate sentence. For the former task, we use Stanford English parser; and for the later task, we use an in-house developed semantic role lab...