LTP-Cloud (Language Technology Platform Cloud), is developed by the Research Center for Social Computing and Information Retrieval at Harbin Harbin Institute of Technology (HIT-SCIR). It is a Cloud based analysis infrastructure, which provides rich, scalable and accuracy natural language processing service. Modules integrated in LTP-cloud includes Chinese word segmentation, POS tagging, dependency parsing, named entity recognition, and semantic role labeling.

As a cloud-based service, LTP-cloud has the following features:

• Free Installation: user only needs to download the LTP-Cloud client source code, compile and run. The results can be obtained without calling a static library or download model file.
• Low Requirements on Hardware: LTP-Cloud client can be run on almost any hardware configuration of the computer. User doesn't need to buy a high-performance machine.
• Programming Language Free, Multiple Platform Supports: LTP Cloud API can be accessed from simple HTTP request, thus, cancealing the dependency of programming language.

The Language Technology Platform (LTP), is an open-sourced Chinese natural language processing system developed in the Research Center for Social Computing and Information Retrieval, HIT. LTP has developed an XML-based natural language processing results expression, and on this basis provides a rich set of bottom-up, efficient, high-precision Chinese natural language processing modules including lexical, syntactic, semantic analysis and other five Chinese processing core technology.

LTP has obtained excellent results in serveral Chinese and international bake-offs. Especially it won the first place in the CoNLL 2009 international joint evaluation of syntactic and semantic analysis. LTP also provides well-documented web service, programming interface and visualization tools.

The academic version of LTP has been shared with more than 500 research institutions free of charge, Baidu, Tencent, Huawei, Kingsoft and other companies pay to use the commercial version of LTP. In 2010, LTP was awarded the highest award in Chinese Information Processing - "Weichang Qian Chinese Information Processing Science and Technology Award."

If your company or institute needs a series of high-performance Chinese natural language analysis toolkit to deal with large-scale data, or you want to lanch your study based on some low-level Chinese natural language processing modules, or you want to compare your own experimental results were with the state-of-the-art work, LTP may be your choice.

Language Technology Platform Architecture

Why we need language analysis

Supposing your company has released a new mobile phone. The release of this new product brought mass reports and user feedback. Facing mess of the data, you may want to know

• What are the features that people mainly concern
• What's the review of the phone
• Who expressed the willingness to buy

Let the machine replace people to complete these analysis work is what natural language processing does.

What kind of language analysis we need

Chinese Word Segmentation (CWS)

Chinese word segmentation (CWS) is the task of segmenting Chinese sentence into word sequence. Since word is the basic language unit that carrying sense in Chinese, word segmentation is the initial step of information retrieval, text classification, sentiment analysis and many other Chinese natural language processing tasks.

Chinese word segmentation becomes a tough task because the combination of characters may introduce ambiguities. For example, for the following sentence:

国务院总理李克强调研上海外高桥时提出，支持上海积极探索新机制。

国务院/ 总理/ 李克强/ 调研/ 上海/ 外高桥/ 时/ 提出/ ，/ 支持/ 上海/ 积极/ 探索/ 新/ 机制/ 。

国务院/ 总理/ 李克/ 强调/ 研/ 上海 …

Ambiguity is a major problem in segmentation task. LTP's word segment module is based on machine learning framework that is powerful to solve the ambiguity problem. Meanwhile, owing the dictionary strategies which incorporated in the model, new words information can be easily handled with sufficient materials.

Part-of-speech Tagging (POS)

Part-of-speech Tagging (POS) is the task of labeling each word in a sentence with its appropriate part of speech. We decide whether each word is a noun, verb, adjective, or whatever. The following sentence is an example of:

国务院/ni 总理/n 李克强/nh 调研/v 上海/ns 外高桥/ns 时/n 提出/v ，/wp 支持/v 上海/ns 积极/a 探索/v 新/a 机制/n 。/wp

The POS Tag of word as a generalization of the word, in speech recognition, parsing, information extraction tasks such an important role. For example, in the extraction "歌曲(songs)" of the related properties, we have a series of phrases:

儿童歌曲 欢快歌曲 各种歌曲 悲伤歌曲 ...

[adjective]歌曲 [noun]歌曲 

The POS sets:The POS module of LTP uses the sets in "863", each of its meaning is listed in following table:

Named Entity Recognition (NER)

NER is the task to locate and identify the names of person, location, orgnization and other entities in a sequence of words in a sentence. As in the previous example, NER results are:

国务院 (organization name) 总理李克强 (person name) 调研上海外高桥 (location name) 时提出，支持上海 (location name) 积极探索新机制。 

NER is important for mining entities in text, which can futher improve extracting events or mining information.

Types of named entity is usually determined by the task. LTP provides the most basic of the three entity type names, places, organization name recognition. Users can easily expanded the ner tags into a brand name of an entity type, software, names, etc.

Dependency Parsing (DP)

By analyzing the dependency relations between language components, dependency parsing reveals its syntactic structure. Intuitively, DP identifies "subject-verb", "adverbial" and other grammatical structure and analyzes the relationship between different components in a sentence. As the example above, the analysis result is:

From the analysis results, we can see that the head predicate in the sentence is "提出", the subject is "李克强" and proposed the object is "支持上海 ...", "调研... 时" is the time adverbial of "支持", "李克强" modifiers "国务院总理", the object of "支持" is "探索 新机制". With the above syntax analysis, we can more easily see that "提出者" is "李克强" instead of "上海" or "外高桥", even if they are nouns and the distance to "提出" is more close.

The annotation of dependency relations (total of 14) and their meanings are listed below:

Semantic Role Labeling (SRL)

SRL is a shallow semantic analysis technology to mark some phrases in a sentence as arguments of a given predicate, such as agent, patient, time and location. SRL can contribute to question answering system, information extraction and machine translation applications. For the above example, the SRL results are:

There are three predicates提出， 调研and探索for example, 探索,积极 is the way (generally expressed by ADV), while 新机制 is its patient (generally expressed by A1).

There are six kinds of the core of the semantic role from A0 to A5, A0 usually indicates the action agent, A1 is usually the impact of that action, A2-5 will be different according to different verb semantic meaning. The remaining 15 semantic roles are the additional semantic roles, such as LOC indicates locations, TMP represents time. Additional semantic roles are listed below:

Semantic Dependency Parsing (SDP)

Given a complete sentence, semantic dependency parsing (SDP) aims at determining all the word pairs related to each other semantically and assigning specific predefined semantic relations, which is a projective tree structure now and will be expanded to directed acyclic graphs. Semantic dependency analysis represents the meaning of sentences by a collection of dependency word pairs and their corresponding relations. This procedure survives from syntactic variation. Here are three sentences:

Their syntactic structures are distinct, while the same meaning is hidden behind these sentences. i.e. eat<he, apple>.

SDP is not influenced by syntactic structures which makes it focus on the real semantics of sentences. Here is an example illustrating the parsing results of DP and SDP respectively.

From the above comparison, we can conclude two points, the first one is syntactic dependencies put emphasis on non-entity words for their roles in partitioning the sentence structures. The second one is that the totally different dependency relations make SDP could represent the detail semantics. SDP can be used to answer the questions raised from the sentences, such as all the Wh-Questions. However, DP could not.

We classify the semantic relations into three categories, i.e. main semantic roles, each one of them has a corresponding reverse relation and a nested relation, event relations and semantic markers. Click here for the details of the Chinese semantic dependency scheme. Next, we list the semantic relations and make each one a simple explanation.

Chinese Word Segmentation (CWS)

Chinese word segmentation is the task of segmenting Chinese sentence into word sequence. Since Chinese word is the minimum unit that carrying sense, Chinese word segmentation became the initial step in many Chinese NLP tasks includeing Information Retrieve, Text classification, Sentiment Analysis.

Owing to the fundamental position of Chinese word segmentation, many studies have been conducted on this task. There are two general types of Chinese word segmentation methods. The first is lexicon matching methods and the second is machine learning based methods. Word segmentation model in LTP combines this two methods. The algorithm used in this model can both benifit from the disambiguity power of statistical machine learning model and widely covered lexicon.

In LTP, the word segmentation problems is modeled as character-based sequence labeling problem. For a given input sentence, the model tags each character with a tag which identifies word boundary.

Meanwhile, in order to improve the performance on the Internet text, especially the micro-blog, the following optimization strategies are incorporated into our word segmentation module:

• a special word like English word and URI are recognized by rules
• spaces and other natural annotated clues are used to help segmenting word
• lexicon information in compiled into statistical model
• mutual information, accessory variety collected from large scale unlabeled data is taken into consideration.

The performance of word segmentation module on People's Daily test sets is shown below:

• Accuracy

	P	R	F
Development set	0.973152	0.972430	0.972791
Test set	0.972316	0.970354	0.972433

• Runtime memory：119m
• Speed：176.91k/s
• Second place in CLP 2012 Bakeoff Task 1: Chinese Word Segmentation on MicroBlog Corpora.

Part-of-speech Tagging (POS)

The same as word segmentation module, POS Tagging can also be modeled as a sequence of labeling problem that labels each word a POS Tag. The tag set we employ in LTP is the Peking University tag set.

The performance of POS module on People's Daily test sets is shown below:

• Settings of Corpus: People's Daily corpus from February to June in 1998 is set as training data (the remaining 10% of data as a development set). The data in January is set as the test data.
• Accuracy:

  	P
  Development set	0.979621
  Test set	0.978337

• Runtime memory：291m
• Speed：106.14k/s

Named Entity Recognition (NER)

The same with word segmentation module, the NER task is modeled as a sequence of labeling task based on word. For input sentence word sequence, the module tag each word in the sentence a named entity identification and entity class tag. LTP support recognize the names of persons, organizations, locations.

the performance is shown below:

• Settings of corpus: People's Daily in January 1998 to do the training (the remained 10% of data as a development set), 6 months ago 10,000 to do the test as training data.
• Accuracy

  P	R	F
  Development set	0.924149	0.909323
  Test set	0.939552	0.936372

• Runtime memory：21m

Dependency Parsing (DP)

Graph-based dependency parsing method is first proposed by the McDonald, he detrive it as the problem to find maximum spanning tree in a graph(Maximum Spanning Tree) .

In dependency parsing module, LTP realized

• first order decoding algorithm(1o)
• second-order decoding alogrithm incorporating sibling (2o-sib)
• second-order decoding alogrithm incorporating sibling and grandson (2o-carreras)

In the LDC data sets, the three different decoding performance is in the following table.

• Ranked the second and third on SANCL 2012 Internet data dependency parsing evaluation
• Ranked third in Chinese dependency parsing on CoNLL 2009 evaluation of syntactic and semantic dependency parsing

Semantic Role Labeling (SRL)

In LTP, we divide SRL into two sub-tasks. Fist is the predicate identification and second is argument identification and classification. For the identification and classification of argument, we treat it as a joint mission, the "non-argument" and the "argument". In SRL system, we introduce the the L1 regular to the maximum entropy model which result in feature dimensions reduce to 1/40 of original one. It greatly improve the memory performance, and enhance the prediction speed. Meanwhile, in order to ensure annotation results meet certain constraints, the system adds post-processing.

In CoNLL 2009 evaluation data set, experiment result using automatic POS tags and syntax information in LTP is shown below.

• First place in CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages joint mission.