Luciano Del Corro

FINET: Context-Aware Fine-Grained Named Entity Typing

We propose FINET, a system for detecting the types of named entities in short inputs—such as sentences or tweets—with respect to WordNet’s super fine-grained type system. FINET generates candidate types using a sequence of multiple extractors, ranging from explicitly mentioned types to implicit types, and subsequently selects the most appropriate using ideas from word-sense disambiguation. FINET combats data scarcity and noise from existing systems: It does not rely on supervision in its extractors and generates training data for type selection from WordNet and other resources. FINET supports the most fine-grained type system so far, including types with no annotated training data. Our experiments indicate that FINET outperforms state-of-the-art methods in terms of recall, precision, and granularity of extracted types.

CORE: Context-Aware Open Relation Extraction with Factorization Machines

We propose CORE, a novel matrix factorization model that leverages contextual information for open relation extraction. Our model is based on factorization machines and integrates facts from various sources, such as knowledge bases or open information extractors, as well as the context in which these facts have been observed. We argue that integrating contextual information—such as metadata about extraction sources, lexical context, or type information—significantly improves prediction performance. Open information extractors, for example, may produce extractions that are unspecific or ambiguous when taken out of context. Our experimental study on a large real-world dataset indicates that CORE has significantly better prediction performance than state-ofthe-art approaches when contextual information is available.

Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning

Word-sense recognition and disambiguation (WERD) is the task of identifying word phrases and their senses in natural language text. Though it is well understood how to disambiguate noun phrases, this task is much less studied for verbs and verbal phrases. We present Werdy, a framework for WERD with particular focus on verbs and verbal phrases. Our framework first identifies multi-word expressions based on the syntactic structure of the sentence; this allows us to recognize both contiguous and non-contiguous phrases. We then generate a list of candidate senses for each word or phrase, using novel syntactic and semantic pruning techniques. We also construct and leverage a new resource of pairs of senses for verbs and their object arguments. Finally, we feed the so-obtained candidate senses into standard word-sense disambiguation (WSD) methods, and boost their precision and recall. Our experiments indicate that Werdy significantly increases the performance of existing WSD methods.

Methods for Open Information Extraction and Sense Disambiguation on Natural Language Text

Natural language text has been the main and most comprehensive way of expressing and storing knowledge. A long standing goal in computer science is to develop systems that automatically understand textual data, making this knowledge accessible to computers and humans alike. We conceive automatic text understanding as a bottom-up approach, in which a series of interleaved tasks build upon each other. Each task achieves more understanding over the text than the previous one. In this regard, we present three methods that aim to contribute to the primary stages of this setting. Our first contribution, ClausIE, is an open information extraction method intended to recognize textual expressions of potential facts in text (e.g. “Dante wrote the Divine Comedy”) and represent them with an amenable structure for computers [(“Dante”, “wrote”, “the Divine Comedy”)]. Unlike previous approaches, ClausIE separates the recognition of the information from its representation, a process that understands the former as universal (i.e., domain-independent) and the later as application-dependent. ClausIE is a principled method that relies on properties of the English language and thereby avoids the use of manually or automatically generated training data. Once the information in text has been correctly identified, probably the most important element in a structured fact is the relation which links its arguments, a relation whose main component is usually a verbal phrase. Our second contribution, Werdy, is a word entry recognition and disambiguation method. It aims to recognize words or multi-word expressions (e.g., “Divine Comedy” is a multi-word expression) in a fact and disambiguate verbs (e.g., what does “write” mean?). Werdy is also an unsupervised approach, mainly relying on the syntactic and semantic relation established between a verb sense and its arguments. The other key components in a structured fact are the named entities (e.g., “Dante”) that often appear in the arguments. FINET, our last contribution, is a named entity typing method. It aims to understand the types or classes of those names entities (e.g., “Dante” refers to a writer). FINET is focused on typing named entities in short inputs (like facts). Unlike previous systems, it is designed to find the types that match the entity mention context (e.g., the fact in which it appears). It uses the most comprehensive type system of any entity typing method to date with more than 16k classes for persons, organizations and locations.