9,478 research outputs found
Why We Need Corpus Linguistics in Intuition-Based Semantics
The following method is popular in some areas of philosophy and linguistics when trying to describe the semantics of a given sentence Φ. Present ordinary speakers with scenarios that involve an utterance of Φ, ask them whether these utterances are felicitous or infelicitous and then construct a semantics that assigns the truth-value True to felicitous utterances of Φ and the truth-value False to infelicitous utterances of Φ. The author makes five observations about this intuition-based approach to semantics; their upshot is that it should be revised in favour of a more nuanced method. The author suggests that this method should be based on corpus linguistics and makes some tentative remarks about what it might look like and which questions we need to address in order to develop it
ANSWERING TOPICAL INFORMATION NEEDS USING NEURAL ENTITY-ORIENTED INFORMATION RETRIEVAL AND EXTRACTION
In the modern world, search engines are an integral part of human lives. The field of Information Retrieval (IR) is concerned with finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need (query) from within large collections (usually stored on computers). The search engine then displays a ranked list of results relevant to our query. Traditional document retrieval algorithms match a query to a document using the overlap of words in both. However, the last decade has seen the focus shifting to leveraging the rich semantic information available in the form of entities. Entities are uniquely identifiable objects or things such as places, events, diseases, etc. that exist in the real or fictional world. Entity-oriented search systems leverage the semantic information associated with entities (e.g., names, types, etc.) to better match documents to queries. Web search engines would provide better search results if they understand the meaning of a query.
This dissertation advances the state-of-the-art in IR by developing novel algorithmsthat understand text (query, document, question, sentence, etc.) at the semantic level. To this end, this dissertation aims to understand the fine-grained meaning of entities from the context in which the entities have been mentioned, for example, “oysters” in the context of food versus ecosystems. Further, we aim to automatically learn (vector) representations of entities that incorporate this fine-grained knowledge and knowledge about the query. This work refines the automatic understanding of text passages using deep learning, a modern artificial intelligence paradigm.
This dissertation utilized the semantic information extracted from entities to retrieve materials (text and entities) relevant to a query. The interplay between text and entities in the text is studied by addressing three related prediction problems: (1) Identify entities that are relevant for the query, (2) Understand an entity’s meaning in the context of the query, and (3) Identify text passages that elaborate the connection between the query and an entity.
The research presented in this dissertation may be integrated into a larger system de-signed for answering complex topical queries such as dark chocolate health benefits which require the search engine to automatically understand the connections between the query and the relevant material, thus transforming the search engine into an answering engine
Why Does This Entity Matter? Finding Support Passages for Entities in Search
In this work, we propose a method to retrieve a human-readable explanation of how a retrieved entity is connected to the information need, analogous to search snippets for document retrieval. Such an explanation is called a support passage.
Our approach is based on the idea: a good support passage contains many entities relevantly related to the target entity (the entity for which a support passage is needed). We define a relevantly related entity as one which (1) occurs frequently in the vicinity of the target entity, and (2) is relevant to the query. We use the relevance of a passage (induced by the relevantly related entities) to find a good support passage for the target entity. Moreover, we want the target entity to be central to the discussion in the support passage. Hence, we explore the utility of entity salience for support passage retrieval and study the conditions under which it can help. We show that our proposed method can improve performance as compared to the current state-of-the-art for support passage retrieval on two datasets from TREC Complex Answer Retrieval
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