Marco Baroni
Biographic Data
| ID | 3844487 |
|---|---|
| NAME | Marco Baroni |
| GIVEN NAMES | Marco |
| FAMILY NAME | Baroni |
| SIGNATURE | BARONI M |
| AFFILIATIONS | University of Trento |
| VERIFIED | No |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 32 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2009 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 1 |
Mechanisms for handling nested dependencies in neural-network language models and humans
When the Whole Is Less Than the Sum of Its Parts
Distributional semantic models, deriving vector-based word representations from patterns of word usage in corpora, have many useful applications (Turney and Pantel 2010 ). Recently, there has been interest in compositional distributional models, which derive vectors for phrases from representations of their constituent words (Mitchell and Lapata 2010 ). Often, the values of distributional vectors are pointwise mutual information (PMI) scores obta…
There Is No Logical Negation Here, But There Are Alternatives
Logical negation is a challenge for distributional semantics, because predicates and their negations tend to occur in very similar contexts, and consequently their distributional vectors are very similar. Indeed, it is not even clear what properties a “negated” distributional vector should possess. However, when linguistic negation is considered in its actual discourse usage, it often performs a role that is quite different from straightforward l…
Grounding Distributional Semantics in the Visual World
Distributional semantic models build vector-based word meaning representations on top of contextual information extracted from large collections of text. Object recognition methods from computer vision derive vector-based representations of visual content from natural images. This article reviews how methods from computer vision are exploited to tackle the fundamental problem ofgroundingdistributional semantic models, bringing them closer to prov…
When the Whole Is Not Greater Than the Combination of Its Parts
Distributional semantics has been extended to phrases and sentences by means of composition operations. We look at how these operations affect similarity measurements, showing that similarity equations of an important class of composition methods can be decomposed into operations performed on the subparts of the input phrases. This establishes a strong link between these models and convolution kernels
Composition in Distributional Semantics
Distributional Semantic Models, which automatically induce word meaning representations from naturally occurring textual data, are a success story of computational linguistics. Recently, there has been much interest in whether such models, endowed with a compositional component, can also successfully approximate the meaning of phrases and sentences. In this article, mostly addressed to theoretical linguists curious about distributional semantics,…
Distributional Memory
Research into corpus-based semantics has focused on the development of ad hoc models that treat single tasks, or sets of closely related tasks, as unrelated challenges to be tackled by extracting different kinds of distributional information from the corpus. As an alternative to this "one task, one model" approach, the Distributional Memory framework extracts distributional information once and for all from the corpus, in the form of a set of wei…
The WaCky wide web
Distributional Memory
Research into corpus-based semantics has focused on the development of ad hoc models that treat single tasks, or sets of closely related tasks, as unrelated challenges to be tackled by extracting different kinds of distributional information from the corpus. As an alternative to this "one task, one model" approach, the Distributional Memory framework extracts distributional information once and for all from the corpus, in the form of a set of wei…
When the Whole Is Less Than the Sum of Its Parts
Distributional semantic models, deriving vector-based word representations from patterns of word usage in corpora, have many useful applications (Turney and Pantel 2010 ). Recently, there has been interest in compositional distributional models, which derive vectors for phrases from representations of their constituent words (Mitchell and Lapata 2010 ). Often, the values of distributional vectors are pointwise mutual information (PMI) scores obta…
Grounding Distributional Semantics in the Visual World
Distributional semantic models build vector-based word meaning representations on top of contextual information extracted from large collections of text. Object recognition methods from computer vision derive vector-based representations of visual content from natural images. This article reviews how methods from computer vision are exploited to tackle the fundamental problem ofgroundingdistributional semantic models, bringing them closer to prov…
When the Whole Is Not Greater Than the Combination of Its Parts
Distributional semantics has been extended to phrases and sentences by means of composition operations. We look at how these operations affect similarity measurements, showing that similarity equations of an important class of composition methods can be decomposed into operations performed on the subparts of the input phrases. This establishes a strong link between these models and convolution kernels
The WaCky wide web
Distributional Memory
Research into corpus-based semantics has focused on the development of ad hoc models that treat single tasks, or sets of closely related tasks, as unrelated challenges to be tackled by extracting different kinds of distributional information from the corpus. As an alternative to this "one task, one model" approach, the Distributional Memory framework extracts distributional information once and for all from the corpus, in the form of a set of wei…
Composition in Distributional Semantics
Distributional Semantic Models, which automatically induce word meaning representations from naturally occurring textual data, are a success story of computational linguistics. Recently, there has been much interest in whether such models, endowed with a compositional component, can also successfully approximate the meaning of phrases and sentences. In this article, mostly addressed to theoretical linguists curious about distributional semantics,…
When the Whole Is Not Greater Than the Combination of Its Parts
Distributional semantics has been extended to phrases and sentences by means of composition operations. We look at how these operations affect similarity measurements, showing that similarity equations of an important class of composition methods can be decomposed into operations performed on the subparts of the input phrases. This establishes a strong link between these models and convolution kernels
When the Whole Is Less Than the Sum of Its Parts
Distributional semantic models, deriving vector-based word representations from patterns of word usage in corpora, have many useful applications (Turney and Pantel 2010 ). Recently, there has been interest in compositional distributional models, which derive vectors for phrases from representations of their constituent words (Mitchell and Lapata 2010 ). Often, the values of distributional vectors are pointwise mutual information (PMI) scores obta…
There Is No Logical Negation Here, But There Are Alternatives
Logical negation is a challenge for distributional semantics, because predicates and their negations tend to occur in very similar contexts, and consequently their distributional vectors are very similar. Indeed, it is not even clear what properties a “negated” distributional vector should possess. However, when linguistic negation is considered in its actual discourse usage, it often performs a role that is quite different from straightforward l…
Grounding Distributional Semantics in the Visual World
Distributional semantic models build vector-based word meaning representations on top of contextual information extracted from large collections of text. Object recognition methods from computer vision derive vector-based representations of visual content from natural images. This article reviews how methods from computer vision are exploited to tackle the fundamental problem ofgroundingdistributional semantic models, bringing them closer to prov…
Mechanisms for handling nested dependencies in neural-network language models and humans
Natural Language Processing Techniques (8 works) · Computer Science (7 works) · Linguistics (7 works) · Natural language processing (7 works) · Artificial Intelligence (6 works) · Topic Modeling (6 works) · Distributional semantics (5 works) · Programming language (4 works) · Philosophy (3 works) · Semantic similarity (3 works)