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 In this section, we
discuss related work in three aspects: text segmentation, POS tagging, and
semantic labeling. Text Segmentation. We consider text segmentation as to
divide a text into a sequence of terms. Statistical approaches, such as N-gram
Model 21, 22, 23, calculate the frequencies of words co-occurring as
neighbors in a training corpus. When the frequency exceeds a prede?ned
threshold, the corresponding neighboring words can be treated as a term.
Vocabulary-based approaches 18, 19, 20 extract terms by checking for
their existence or frequency in a prede?ned vocabulary. The most obvious
drawback of existing methods for text segmentation is that they only consider
surface features and ignore the requirement of semantic coherence within a
segmentation. This might lead to incorrect segmentations as described in
Challenge 1. To this end, we propose to exploit context semantics when
conducting text segmentation. POS tagging. POS tagging determines lexical types
(i.e., POS tags) of words in a text. Rule-based POS taggers attempt to assign
POS tags to unknown or ambiguous words based on a large number of hand-crafted
10, 11 or automatically learned 12, 13 linguistic rules. Statistical
POS taggers avoid the cost of constructing tagging rules by building a
statistical model automatically from a corpora and labeling untagged texts
based on those learned statistical information. Mainstream statistical POS
taggers employ the well-known Markov Model 14, 15, 16, 17 which learns
both lexical probabilities and sequential probabilities from a labeled corpora
and tags a new sentence by searching for tag sequence that maximizes the
combination of lexical and sequential probabilities. Note that both rule-based
and statistical POS taggers rely on the assumption that texts are correctly
structured which, however, is not always the case for short texts. More
importantly, existing methods only considers lexical features and ignores word
semantics. This might lead to mistakes, as illustrated in Challenge 3. Our work
attempts to build a tagger which considers both lexical features and underlying
semantics for type detection. Semantic labeling. Semantic labeling discovers
hidden semantics from a natural language text. Named entity recognition (NER)
locates named entities in a text and classi?es them into prede?ned categories
(e.g., persons, organizations, locations, etc.) using linguistic grammar-based
techniques as well as statistical models like CRF 1 and HMM 2. Topic models
3 attempt to recognize “latent topics”, which are represented as
probabilistic distributions on words, based on observable statistical relations
between texts and words. Entity linking 5, 6, 7, 8 employs existing
knowledgebases and focuses on retrieving “explicit topics” expressed as
probabilistic distributions on the entire knowledge base. Despite the high
accuracy achieved by existing work on semantic labeling, there are still some
limitations. First, categories, “latent topics”, and “explicit topics” are
different from human-understandable concepts. Second, short texts do not always
observe the syntax of a written language which, however, is an indispensable
feature for mainstream NER tools. Third, short texts do not contain suf?cient
content to support statistical models like topic models. The work most related
to ours are conducted by Song et al. 19 and Kim et al. 20 respectively,
which also represent semantics as concepts. 19 employs the Bayesian Inference
mechanism to conceptualize instances and short texts, and eliminates instance
ambiguity based on homogeneous instances. Kim et al. 20 captures semantic
relatedness between instances using a probabilistic topic model (i.e., LDA),
and disambiguates instances based on related instances. In this work, we
observe that other terms, such as verbs, adjectives, and attributes, can also
help with instance disambiguation. We incorporate type discernment in to our
framework for short text understanding of conduct instance disambiguate based
on various types of context information.

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