27,770 research outputs found
Combining independent modules to solve multiple-choice synonym and analogy problems
Existing statistical approaches to natural language problems are very
coarse approximations to the true complexity of language processing.
As such, no single technique will be best for all problem instances.
Many researchers are examining ensemble methods that combine the
output of successful, separately developed modules to create more
accurate solutions. This paper examines three merging rules for
combining probability distributions: the well known mixture rule, the
logarithmic rule, and a novel product rule. These rules were applied
with state-of-the-art results to two problems commonly used to assess
human mastery of lexical semantics -- synonym questions and analogy
questions. All three merging rules result in ensembles that are more
accurate than any of their component modules. The differences among the
three rules are not statistically significant, but it is suggestive
that the popular mixture rule is not the best rule for either of the
two problems
Translating near-synonyms: Possibilities and preferences in the interlingua
This paper argues that an interlingual representation must explicitly
represent some parts of the meaning of a situation as possibilities (or
preferences), not as necessary or definite components of meaning (or
constraints). Possibilities enable the analysis and generation of nuance,
something required for faithful translation. Furthermore, the representation of
the meaning of words, especially of near-synonyms, is crucial, because it
specifies which nuances words can convey in which contexts.Comment: 8 pages, LaTeX2e, 1 eps figure, uses colacl.sty, epsfig.sty, avm.sty,
times.st
Toward Entity-Aware Search
As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability
Mining the Web for Lexical Knowledge to Improve Keyphrase Extraction: Learning from Labeled and Unlabeled Data.
A journal article is often accompanied by a list of keyphrases, composed of about five to fifteen important words and phrases that capture the articleÂ’s main topics. Keyphrases are useful for a variety of purposes, including summarizing, indexing, labeling, categorizing, clustering, highlighting, browsing, and searching. The task of automatic keyphrase extraction is to select keyphrases from within the text of a given document. Automatic keyphrase extraction makes it feasible to generate keyphrases for the huge number of documents that do not have manually assigned keyphrases. Good performance on this task has been obtained by approaching it as a supervised learning problem. An input document is treated as a set of candidate phrases that must be classified as either keyphrases or non-keyphrases. To classify a candidate phrase as a keyphrase, the most important features (attributes) appear to be the frequency and location of the candidate phrase in the document. Recent work has demonstrated that it is also useful to know the frequency of the candidate phrase as a manually assigned keyphrase for other documents in the same domain as the given document (e.g., the domain of computer science). Unfortunately, this keyphrase-frequency feature is domain-specific (the learning process must be repeated for each new domain) and training-intensive (good performance requires a relatively large number of training documents in the given domain, with manually assigned keyphrases). The aim of the work described here is to remove these limitations. In this paper, I introduce new features that are conceptually related to keyphrase-frequency and I present experiments that show that the new features result in improved keyphrase extraction, although they are neither domain-specific nor training-intensive. The new features are generated by issuing queries to a Web search engine, based on the candidate phrases in the input document. The feature values are calculated from the number of hits for the queries (the number of matching Web pages). In essence, these new features are derived by mining lexical knowledge from a very large collection of unlabeled data, consisting of approximately 350 million Web pages without manually assigned keyphrases
A Study of Metrics of Distance and Correlation Between Ranked Lists for Compositionality Detection
Compositionality in language refers to how much the meaning of some phrase
can be decomposed into the meaning of its constituents and the way these
constituents are combined. Based on the premise that substitution by synonyms
is meaning-preserving, compositionality can be approximated as the semantic
similarity between a phrase and a version of that phrase where words have been
replaced by their synonyms. Different ways of representing such phrases exist
(e.g., vectors [1] or language models [2]), and the choice of representation
affects the measurement of semantic similarity.
We propose a new compositionality detection method that represents phrases as
ranked lists of term weights. Our method approximates the semantic similarity
between two ranked list representations using a range of well-known distance
and correlation metrics. In contrast to most state-of-the-art approaches in
compositionality detection, our method is completely unsupervised. Experiments
with a publicly available dataset of 1048 human-annotated phrases shows that,
compared to strong supervised baselines, our approach provides superior
measurement of compositionality using any of the distance and correlation
metrics considered
Incremental dimension reduction of tensors with random index
We present an incremental, scalable and efficient dimension reduction
technique for tensors that is based on sparse random linear coding. Data is
stored in a compactified representation with fixed size, which makes memory
requirements low and predictable. Component encoding and decoding are performed
on-line without computationally expensive re-analysis of the data set. The
range of tensor indices can be extended dynamically without modifying the
component representation. This idea originates from a mathematical model of
semantic memory and a method known as random indexing in natural language
processing. We generalize the random-indexing algorithm to tensors and present
signal-to-noise-ratio simulations for representations of vectors and matrices.
We present also a mathematical analysis of the approximate orthogonality of
high-dimensional ternary vectors, which is a property that underpins this and
other similar random-coding approaches to dimension reduction. To further
demonstrate the properties of random indexing we present results of a synonym
identification task. The method presented here has some similarities with
random projection and Tucker decomposition, but it performs well at high
dimensionality only (n>10^3). Random indexing is useful for a range of complex
practical problems, e.g., in natural language processing, data mining, pattern
recognition, event detection, graph searching and search engines. Prototype
software is provided. It supports encoding and decoding of tensors of order >=
1 in a unified framework, i.e., vectors, matrices and higher order tensors.Comment: 36 pages, 9 figure
Similarity of Semantic Relations
There are at least two kinds of similarity. Relational similarity is
correspondence between relations, in contrast with attributional similarity,
which is correspondence between attributes. When two words have a high
degree of attributional similarity, we call them synonyms. When two pairs
of words have a high degree of relational similarity, we say that their
relations are analogous. For example, the word pair mason:stone is analogous
to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA),
a method for measuring relational similarity. LRA has potential applications in many
areas, including information extraction, word sense disambiguation,
and information retrieval. Recently the Vector Space Model (VSM) of information
retrieval has been adapted to measuring relational similarity,
achieving a score of 47% on a collection of 374 college-level multiple-choice
word analogy questions. In the VSM approach, the relation between a pair of words is
characterized by a vector of frequencies of predefined patterns in a large corpus.
LRA extends the VSM approach in three ways: (1) the patterns are derived automatically
from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency
data, and (3) automatically generated synonyms are used to explore variations of the
word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the
average human score of 57%. On the related problem of classifying semantic relations, LRA
achieves similar gains over the VSM
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