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James Fan

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8 papers
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8

IJCAI Conference 2016 Conference Paper

Building Joint Spaces for Relation Extraction

  • Chang Wang
  • LiangLiang Cao
  • James Fan

In this paper, we present a novel approach for relation extraction using only term pairs as the input without textual features. We aim to build a single joint space for each relation which is then used to produce relation specific term embeddings. The proposed method fits particularly well for domains in which similar arguments are often associated with similar relations. It can also handle the situation when the labeled data is limited. The proposed method is evaluated both theoretically with a proof for the closed-form solution and experimentally with promising results on both DBpedia and medical relations.

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AAAI Conference 2016 Conference Paper

Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games Using Convolutional Networks

  • Nikolai Yakovenko
  • LiangLiang Cao
  • Colin Raffel
  • James Fan

Poker is a family of card games that includes many variations. We hypothesize that most poker games can be solved as a pattern matching problem, and propose creating a strong poker playing system based on a unified poker representation. Our poker player learns through iterative self-play, and improves its understanding of the game by training on the results of its previous actions without sophisticated domain knowledge. We evaluate our system on three poker games: single player video poker, two-player Limit Texas Hold’em, and finally two-player 2-7 triple draw poker. We show that our model can quickly learn patterns in these very different poker games while it improves from zero knowledge to a competitive player against human experts. The contributions of this paper include: (1) a novel representation for poker games, extendable to different poker variations, (2) a Convolutional Neural Network (CNN) based learning model that can effectively learn the patterns in three different games, and (3) a self-trained system that significantly beats the heuristic-based program on which it is trained, and our system is competitive against human expert players.

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AAAI Conference 2011 Conference Paper

Leveraging Wikipedia Characteristics for Search and Candidate Generation in Question Answering

  • Jennifer Chu-Carroll
  • James Fan

Most existing Question Answering (QA) systems adopt a type-and-generate approach to candidate generation that relies on a pre-defined domain ontology. This paper describes a type independent search and candidate generation paradigm for QA that leverages Wikipedia characteristics. This approach is particularly useful for adapting QA systems to domains where reliable answer type identification and typebased answer extraction are not available. We present a threepronged search approach motivated by relations an answerjustifying title-oriented document may have with the question/answer pair. We further show how Wikipedia metadata such as anchor texts and redirects can be utilized to effectively extract candidate answers from search results without a type ontology. Our experimental results show that our strategies obtained high binary recall in both search and candidate generation on TREC questions, a domain that has mature answer type extraction technology, as well as on Jeopardy! questions, a domain without such technology. Our high-recall search and candidate generation approach has also led to high overall QA performance in Watson, our end-to-end system.

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KR Conference 2004 Conference Paper

A Question-Answering System for AP Chemistry: Assessing KR&R Technologies

  • Ken Barker
  • Vinay Chaudhri
  • Jason Chaw
  • Peter Clark
  • James Fan
  • David Israel
  • Sunil Mishra
  • Bruce Porter

Basic research in knowledge representation and reasoning (KR&R) has steadily advanced over the years, but it has been difficult to assess the capability of fielded systems derived from this research. In this paper, we present a knowledge-based question-answering system that we developed as part of a broader effort by Vulcan Inc. to assess KR&R technologies, and the result of its assessment. The challenge problem presented significant new challenges for knowledge representation, compared with earlier such assessments, due to the wide variability of question types that the system was expected to answer. Our solution integrated several modern KR&R technologies, in particular semantically well-defined frame systems, automatic classification methods, reusable ontologies, a methodology for knowledge base construction, and a novel extension of methods for explanation generation. The resulting system exhibited high performance, achieving scores for both accuracy and explanation which were comparable to human performance on similar tests. While there are qualifications to this result, it is a significant achievement and an informative data point about the state of the art in KR&R, and reflects significant progress by the field.

AAAI Conference 2004 Conference Paper

Interpreting Loosely Encoded Questions

  • James Fan
  • Bruce Porter

Knowledge-based question-answering systems have become quite competent and robust at answering a wide range of questions in different domains, however in order to ask questions correctly, one needs to have intimate knowledge of the structure of the knowledge base, and typical users lack this knowledge. We address this problem by developing a system that uses the content of the knowledge base to automatically align a user’s encoding of a query to the structure of the knowledge base. Our preliminary evaluation shows the system detects and corrects most misalignments, and users are able to pose most questions quickly.

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IJCAI Conference 2003 Conference Paper

The Knowledge Required to Interpret Noun Compounds

  • James Fan
  • Ken Barker
  • Bruce Porter

Noun compound interpretation is the task of determining the semantic relations among the constituents of a noun compound. For example, "concrete floor" means a floor made of concrete, while "gymnasium floor" is the floor region of a gymnasium. We would like to enable knowledge acquisition systems to interpret noun compounds, as part of their overall task of translating imprecise and incomplete information into formal representations that support automated reasoning. However, if interpreting noun compounds requires detailed knowledge of the constituent nouns, then it may not be worth doing: the cost of acquiring this knowledge may outweigh the potential benefit. This paper describes an empirical investigation of the knowledge required to interpret noun compounds. It concludes that the axioms and ontological distinctions important for this task are derived from the top levels of a hierarchical knowledge base (KB); detailed knowledge of specific nouns is less important. This is good news, not only for our work on knowledge acquisition systems, but also for research on text understanding, where noun compound interpretation has a long history. A more detailed version of this paper can be found in [Fan et al, 2003].

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