The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

Probabilistic logical languages provide powerful formalisms forknowledge representation and learning. Yet performing inference inthese languages is extremely costly, especially if it is done at thepropositional level. Lifted inference algorithms, which avoid repeatedcomputation by treating indistinguishable groups of objects as one, helpmitigate this cost. Seeking inspiration from logical inference, wherelifted inference (e.g., resolution) is commonly performed, we developa model theoretic approach to probabilistic lifted inference. Our algorithmcompiles a first-order probabilistic theory into a first-orderdeterministic decomposable negation normal form (d-DNNF) circuit.Compilation offers the advantage that inference is polynomial in thesize of the circuit. Furthermore, by borrowing techniques from theknowledge compilation literature our algorithm effectively exploitsthe logical structure (e.g., context-specific independencies) withinthe first-order model, which allows more computation to be done at the lifted level.An empirical comparison demonstrates the utility of the proposed approach.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

The paper describes a method of relation extraction, which is based on parsing the input text using a combination of a generic HPSG-based grammar and a highly focused domain- and relation-specific lexicon. We also show a method of unsupervised acquisition of such a lexicon from a large unlabeled corpus. Together, the methods introduce a novel approach to the “Open IE” task, which is superior in accuracy and in quality of relation identification to the existing approaches.

An online double auction mechanism for dynamic environments, especially dynamic has to match sellers and buyers dynamically and calculate double auctions. After a brief review of related a payment for each matched trader without knowing work, we specify the problem we are tackling, and about future orders. Such uncertainty is more challenging for then briefly outline our research plan, the results we double auction mechanism design because modelling traders' have achieved to date, and the ongoing directions.

The most prominent way to establish trust in online markets such as eBay are reputation systems that publish buyer feedback about a seller's past behavior. These systems, however, critically rely on assumptions that are rarely met in real-world marketplaces: first, it is assumed that there are no reporting costs and no benefits from lying so that buyers honestly report their private experiences. Second, it is assumed that every seller is long-lived, i.e. will continue to trade on the marketplace indefinitely and, third, it is assumed that sellers cannot whitewash, i.e. create new accounts once an old one is ran down. In my thesis, I address all of these assumptions and design incentive-compatible trust mechanisms that do not rely on any of the aforementioned assumptions. Moreover, I focus on designs that minimize common knowledge assumptions with respect to the players' valuations, costs and beliefs.