Unsupervised Discovery of Domain-Specific Knowledge from Text

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1466–1475, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Unsupervised Discovery of Domain-Specific Knowledge from Text Dirk Hovy, Chunliang Zhang, Eduard Hovy Information Sciences Institute University of Southern California 4676 Admiralty Way, Marina del Rey, CA 90292 {dirkh, czheng, hovy}@isi.edu Anselmo Pe˜nas UNED NLP and IR Group Juan del Rosal 16 28040 Madrid, Spain anselmo@lsi.uned.es Abstract Learning by Reading (LbR) aims at enabling machines to acquire knowledge from and rea- son about textual input. This requires knowl- edge about the domain structure (such as en- tities, classes, and actions) in order to do in- ference. We present a method to infer this im- plicit knowledge from unlabeled text. Unlike previous approaches, we use automatically ex- tracted classes with a probability distribution over entities to allow for context-sensitive la- beling. From a corpus of 1.4m sentences, we learn about 250k simple propositions about American football in the form of predicate- argument structures like “quarterbacks throw passes to receivers”. Using several statisti- cal measures, we show that our model is able to generalize and explain the data statistically significantly better than various baseline ap- proaches. Human subjects judged up to 96.6% of the resulting propositions to be sensible. The classes and probabilistic model can be used in textual enrichment to improve the per- formance of LbR end-to-end systems. 1 Introduction The goal of Learning by Reading (LbR) is to enable a computer to learn about a new domain and then to reason about it in order to perform such tasks as question answering, threat assessment, and explana- tion (Strassel et al., 2010). This requires joint efforts from Information Extraction, Knowledge Represen- tation, and logical inference. All these steps depend on the system having access to basic, often unstated, foundational knowledge about the domain. Most documents, however, do not explicitly men- tion this information in the text, but assume basic background knowledge about the domain, such as positions (“quarterback”), titles (“winner”), or ac- tions (“throw”) for sports game reports. Without this knowledge, the text will not make sense to the reader, despite being well-formed English. Luckily, the information is often implicitly contained in the document or can be inferred from similar texts. Our system automatically acquires domain- specific knowledge (classes and actions) from large amounts of unlabeled data, and trains a probabilis- tic model to determine and apply the most infor- mative classes (quarterback, etc.) levels of generality for unseen data.at appropriate E.g., from sentences such as “Steve Young threw a pass to Michael Holt”, “Quarterback Steve Young finished strong”, and “Michael Holt, the receiver, left early” we can learn the classes quarterback and receiver, and the proposition “quarterbacks throw passes to receivers”. We will thus assume that the implicit knowl- edge comes in two forms: actions in the form of predicate-argument structures, and classes as part of the source data. Our task is to identify and extract both. Since LbR systems must quickly adapt and scale well …

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