Article Structure
Abstract
The validity of semantic inferences depends on the contexts in which they are applied.
Introduction
Applied semantic inference is typically concerned with inferring a target meaning from a given text.
Contextual Preferences
2.1 Notation
Contextual Preferences Models
This section presents the current models that we implemented for the various components of the CP framework.
Experimental Settings
Evaluating the contribution of Contextual Preferences models requires: (a) a sample of test hypotheses, and (b) a corresponding corpus that contains sentences which entail these hypotheses, where all hypothesis matches (either direct or via rules) are annotated.
Results and Analysis
We experimented with three rule setups over the ACE dataset, in order to measure the contribution of the CP framework.
Related Work
Context sensitive inference was mainly investigated in an application-dependent manner.
Conclusions
We presented the Contextual Preferences (CP) framework for assessing the validity of inferences in context.
Topics
statistically significantly
- Our main result is that the allCP and allCP+pr methods rank matches statistically significantly better than the baselines in all setups (according to the Wilcoxon double-sided signed-ranks test at the level of 0.01 (Wilcoxon, 1945)).Page 6, “Results and Analysis”
- Furthermore, relative to this cpv(7“, 25) model from (Pantel et al., 2007), our combined allCP model, with or without the prior (first row of Table 2), obtains statistically significantly better ranking (at the level of 0.01).Page 7, “Results and Analysis”
- Comparing between the algorithms for matching 0pm (Section 3.2.2) we found that while mnkedC B C is statistically significantly better than binaryCBC, mnkedCBC and LIN generally achieve the same results.Page 7, “Results and Analysis”
- * Indicates statistically significant changes compared to the baseline,Page 8, “Results and Analysis”
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contextual information
- Overall, such incorrect inferences may be avoided by considering contextual information for t, h and 7“ during their matching process.Page 2, “Contextual Preferences”
- In this framework, the representation of an object 2, where 2 may be a text, a template or an entailment rule, is enriched with contextual information denoted cp(z).Page 2, “Contextual Preferences”
- CP enriches the representation of textual objects with typical contextual information that constrains or disambiguates their meaning, and provides matching functions that compare the preferences of objects involved in the inference.Page 8, “Conclusions”
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entity types
- We identify entity types using the default Lingpipe2 Named-Entity Recognizer (NER), which recognizes the types Location, Person and Organization.Page 4, “Contextual Preferences Models”
- A variable j has a single preferred entity type in cpv;n(t)[j], the type of its instantiation in 75.Page 4, “Contextual Preferences Models”
- The Contextual Preferences for h were constructed manually: the named-entity types for cpvm(h) were set by adapting the entity types given in the guidelines to the types supported by the Ling-pipe NER (described in Section 3.2).Page 5, “Experimental Settings”
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highest scoring
- The score of matching two 0pm lists, denoted here SCBC(-, -), is the score of the highest scoring member that appears in both lists.Page 4, “Contextual Preferences Models”
- However, it can have several preferred types for h. When matching h with t, j’s match score is that of its highest scoring type, and the final score is the product of all variable scores: mv;n(h,t) = 11-60mm) (maXaeCth) [j] [8(a) va:n(t) UM)-Page 4, “Contextual Preferences Models”
- the highest score in the table.Page 7, “Results and Analysis”
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NER
- We identify entity types using the default Lingpipe2 Named-Entity Recognizer ( NER ), which recognizes the types Location, Person and Organization.Page 4, “Contextual Preferences Models”
- To construct cpv;n(r), we currently use a simple approach where each individual term in cpv;e(r) is analyzed by the NER system, and its type (if any) is added to ammo“).Page 4, “Contextual Preferences Models”
- The Contextual Preferences for h were constructed manually: the named-entity types for cpvm(h) were set by adapting the entity types given in the guidelines to the types supported by the Ling-pipe NER (described in Section 3.2).Page 5, “Experimental Settings”
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