Text Mining Clause Samples

A Text Mining clause defines the rights and limitations regarding the automated analysis of textual data within the scope of an agreement. Typically, it specifies whether parties are permitted to use software tools to extract information, patterns, or insights from large volumes of text, such as documents, articles, or databases provided under the contract. This clause is essential for clarifying whether such activities are allowed, thereby preventing disputes over data usage and ensuring both parties understand the boundaries of permitted data analysis.
Text Mining. Authorized Users may use the Licensed Material to perform and engage in text mining /data mining activities for legitimate academic research and other educational purposes.
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Text Mining. Participating Institutions and their Authorized Users may, subject to prior notification and approval by the licensor, using reasonable practices, engage in text processing, which is any kind of analysis of natural language text. The Licensor will make appropriate arrangements prior to the start of this activity to account for heavy usage and ensure continued access for the user. This may include but not be limited to a process by which information may be derived from text by identifying patterns and trends within natural language through text categorization, statistical pattern recognition, concept or sentiment extraction, and the association of natural language with indexing terms. Technology will not be used to hinder any uses granted under this section.
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Text Mining. We apply a pattern-based natural language process- ing approach for finding exceptions in contract text. Pattern-based information extraction has been an ac- tive discipline in the past two decades. Despite their simplicity, linguistic pattern-based approaches yield surprisingly good results. We survey some important work in this area. Hearst [6] pioneered the pattern-based approach by using it for automatic acquisition of hypernyms from Grolier’s American Academic Encyclopedia. The hyponymy relation such as of apple to fruit indicates the is a relation. To extract such information, Hearst defines patterns of the type pNP0 such as NP 1’. For example, the phrase fruit such as apple (if sufficiently frequent) conveys information that apple is a hy- ponym of fruit. ▇▇▇▇▇▇▇ and Charniak [38] apply a similar pattern- based approach to find nouns that satisfy part-of re- lations in the LDC North American News Corpus (NANC). The part-of relation indicates part and whole of the entities such as wheel to car. ▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇▇’▇ patterns are of the type pNP 0 of NP1’, which indicate a part-of relationship, as in basement of building that basement is a part of building. ▇▇▇▇▇ and Moldova [39] extract causal relations from text using an approach similar to the above on the TREC-9 data set, which is a collection of news articles. To extract causal relations from corpora, ▇▇▇▇▇ and Moldova use the most explicit intra-sentential pattern pNP0 V NP 1’, where V is a simple causative verb. Hearst evaluates her approach against WordNet and obtains a precision of 57.55%. ▇▇▇▇▇▇▇ and Char- niak’s approach yields 55% accuracy for the top 50 words, when evaluated against human annotated data. And, ▇▇▇▇▇ and Moldova achieve 65.6% accu- racy against the average performance on two human annotators on 300 relation pairs. In this context, our results of nearly 90% precision indicate that contracts are a promising domain and perhaps that additional information can be mined from them. ▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇▇ [40] use Hearst patterns [6] to mine business risk vocabularies and build a taxon- omy. They identify potential risks in financial reports. ▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇▇ use the Web as their corpus for vocabulary discovery and validation. In contrast, our system uses a set of contracts as its corpus, and its vocabulary discovery process is not based on the Hearst patterns. ▇▇▇▇▇▇▇▇ and ▇▇▇▇▇▇▇ [41] use an approach based on machine learning to study contract documents. They employ a binary s...
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Text Mining. The purpose of text mining is to process unstructured textual information and extract meaningful numerical indices from the text, in order to make the information contained in the text accessible to the different data mining algorithms (statistical and machine learning) (Aggarwal 2012). Inside text mining, similarity detection (i.e., detection of similar texts by using either their syntactic or semantic properties) is an established field. In (▇▇▇▇ 2007), similarity is used to automatically predict the fixing effort, i.e., the person-hours spent on fixing an issue, such as a software bug. Given a new issue report, the Lucene4 framework is used to query the database of resolved issues for textually similar reports (using the nearest neighbour approach) and use their average time as a prediction. Assignments of developers to bug reports has also been tackled from a similarity perspective: ● ▇▇▇▇ (2009) presents a framework for automated assignment of bug-fixing tasks which infers knowledge about a developer's expertise by analysing the history of bugs previously resolved by the developer. Then, it applies a vector space model (VSM) to recommend experts for fixing bugs, matching the new bug VSM representation with the most similar developer VSM representation. In addition to similarity, other heuristics are taken into account, as current workload and preferences of the developer. 3 ▇▇▇▇://▇▇▇▇▇▇.▇▇▇▇▇▇.▇▇▇/ 4 ▇▇▇▇▇://▇▇▇▇▇▇.▇▇▇▇▇▇.▇▇▇/core/ ● ▇▇▇▇▇▇▇ (2012) proposes an algorithm to discover experts for fixing new software bugs which is based on the analysis of their textual information (e.g., summary and description attributes). Frequent terms are generated from this textual information and then term similarity is used to identify appropriate experts (developers) for the newly reported software bug. Text mining is used in combination with machine learning techniques in (Menzies 2008) to assist test engineers in assigning severity levels to defect reports. The proposed algorithm is based on the automated extraction and analysis of textual descriptions from issue reports: text mining techniques are used to extract the relevant features of each report, while machine learning techniques are used to assign these features with proper severity levels (taking into account the severity levels already assigned to other issues to construct rules about when an specific defect level should be assigned).
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Related to Text Mining

  • Data Mining 4.1. Provider agrees not to use GLO Data for unrelated commercial purposes, advertising or advertising-related services, or for any other purpose not explicitly authorized by the GLO in this Contract or any document related thereto. 4.2. Provider agrees to take all reasonably feasible physical, technical, administrative, and procedural measures to ensure that no unauthorized use of GLO Data occurs.

  • LOKASI ▇▇▇ KETERANGAN HARTANAH Hartanah tersebut adalah terletak di tingkat 6 Pangsapuri Mesra Ria. Hartanah tersebut adalah pangsapuri kos rendah 3 ▇▇▇▇▇ tidur pertengahan dikenali sebagai ▇▇▇▇▇ Pemaju No. A-06-06, Tingkat No 06, Bangunan No A, Pandan Mesra ▇▇▇ beralamat pos di No. 06-06, Pangsapuri Mesra Ria, ▇▇▇▇▇ ▇▇▇▇ ▇▇▇▇, ▇▇▇▇▇ ▇▇▇▇▇▇ ▇▇▇▇▇, ▇▇▇▇▇ ▇▇▇▇▇▇, ▇▇▇▇▇▇▇▇ ▇▇▇▇▇ ▇▇▇▇▇. (“Hartanah”) Hartanah ini akan dijual keadaan “sepertimana sedia ada” tertakluk kepada satu harga rizab sebanyak RM150,000.00 (RINGGIT MALAYSIA SATU RATUS ▇▇▇ ▇▇▇▇ PULUH RIBU SAHAJA), mengikut kepada Syarat-syarat Jualan di sini dengan cara Penyerahan Hak dari Pemegang Serahhak ▇▇▇ tertakluk kepada Pembeli memperoleh pengesahan / kebenaran yang diperlukan daripada Pemaju ▇▇▇/atau Pemilik Tanah ▇▇▇/atau Pihak Berkuasa Negeri ▇▇▇/atau badan-badan yang relevan (jika ada). Semua penawar yang ingin membuat tawaran adalah dikehendaki membayar deposit sebanyak 10% daripada harga rizab (“deposit pendahuluan”) secara bank draf atau kasyier order dipalang “AKAUN PENERIMA SAHAJA” atas nama HONG ▇▇▇▇▇ BANK BERHAD / ▇▇▇ ▇▇▇ ▇▇▇▇ & ▇▇▇▇ ▇▇▇ MEE @ ▇▇▇▇ NYUIK THAI atau melalui pemindahan perbankan atas talian yang ditentukan oleh pelelong, sekurang-kurangnya SATU (1) HARI BEKERJA SEBELUM TARIKH LELONGAN ▇▇▇ membayar perbezaan di antara deposit pendahuluan ▇▇▇ jumlah bersamaan 10% daripada harga berjaya tawaran sama ada dengan bank draf atau kasyier order dipalang “AKAUN PENERIMA SAHAJA” atas nama ▇▇▇▇ ▇▇▇▇▇ BANK BERHAD / ▇▇▇ ▇▇▇ ▇▇▇▇ & ▇▇▇▇ ▇▇▇ MEE @ ▇▇▇▇ NYUIK THAI atau melalui pemindahan perbankan atas talian dalam masa TIGA (3) HARI BEKERJA sebaik sahaja ketukan tukul oleh Pelelong dibuat. Deposit ▇▇▇▇ ▇▇▇ jumlah perbezaan secara dikumpul dikenali sebagai “deposit”. Hari Bekerja bermaksud hari (tidak termasuk Sabtu, Ahad ▇▇▇ ▇▇▇▇ Umum) di mana Pihak Pemegang Serahhak dibuka untuk perniagaan di Kuala Lumpur Baki harga belian sepenuhnya hendaklah dibayar dalam tempoh sembilan puluh (90) hari dari tarikh jualan lelongan kepada HONG ▇▇▇▇▇ BANK BERHAD. ▇▇▇▇ rujuk Terma & Syarat Dalam Talian Pelelong di ▇▇▇.▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇.▇▇▇ untuk ▇▇▇▇-▇▇▇▇ pembayaran deposit. Untuk butir-butir lanjut, ▇▇▇▇ berhubung dengan Tetuan ▇▇▇ ▇▇▇▇ & Co., Peguamcara bagi Pihak Pemegang Serahhak di ▇-▇, ▇▇▇▇▇ ▇▇▇ ▇/▇, ▇▇▇▇▇▇▇ ▇▇▇▇▇▇▇, ▇▇▇▇▇▇ ▇▇▇▇▇▇, ▇▇▇▇▇ ▇▇▇▇▇▇▇▇ ▇▇▇▇, ▇▇▇▇▇▇▇▇. (Ref No.: 51303.23, Tel No.: ▇▇-▇▇▇▇▇▇▇▇, Fax No.: ▇▇-▇▇▇▇▇▇▇▇) atau Pelelong yang tersebut di bawah ini:- Suite C-20-3A, Level 20, Block C, Megan Avenue II, / ▇▇▇▇▇ ▇▇▇▇▇ BIN ▇▇▇▇▇▇ ▇▇, ▇▇▇▇▇ ▇▇▇ ▇▇▇▇ ▇▇▇▇, 50450 Kuala Lumpur. (Pelelong Berlesen) Tel No : ▇▇-▇▇▇▇ ▇▇▇▇ Fax No: ▇▇-▇▇▇▇ ▇▇▇▇ Ruj. Kami: ALIN/HLBB1604/WCC Ruj Bank : ▇▇▇▇▇▇▇▇▇▇ ▇▇▇▇▇ Web: ▇▇▇.▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇.▇▇▇ E-mail : ▇▇▇▇▇▇@▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇.▇▇▇

  • meminta nasihat daripada Pihak ▇▇▇▇▇▇ dalam semua perkara berkenaan dengan jualan lelongan, termasuk Syarat-syarat Jualan (iii) membuat carian Hakmilik ▇▇▇▇▇ ▇▇▇▇▇▇ rasmi di Pejabat Tanah ▇▇▇/atau ▇▇▇▇-▇▇▇▇ Pihak-pihak Berkuasa yang berkenaan ▇▇▇ (iv) membuat pertanyaan dengan Pihak Berkuasa yang berkenaan samada jualan ini terbuka kepada semua bangsa atau kaum Bumiputra Warganegara Malaysia sahaja atau melayu sahaja ▇▇▇ juga mengenai persetujuan untuk jualan ini sebelum jualan lelong.Penawar yang berjaya ("Pembeli") dikehendaki dengan segera memohon ▇▇▇ mendapatkan kebenaran pindahmilik (jika ada) daripada Pihak Pemaju ▇▇▇/atau Pihak Tuanpunya ▇▇▇/atau Pihak Berkuasa Negeri atau badan-badan berkenaan (v) memeriksa ▇▇▇ memastikan samada jualan ini dikenakan cukai. HAKMILIK : Hakmilik strata bagi hartanah ini masih belum dikeluarkan HAKMILIK INDUK / NO. LOT : Pajakan Negeri 35263, Lot No.29096 MUKIM/DAERAH/NEGERI : Setapak / Kuala Lumpur / Wilayah Persekutuan Kuala Lumpur PEGANGAN : Pajakan selama 82-tahun berakhir pada 08/08/2085 KELUASAN LANTAI : 81.104 meter persegi ( 873 kaki persegi ) PEMAJU/PENJUAL : Mega Planner Jaya Sdn Bhd (326287-W)(Dalam Likuidasi) TUANPUNYA : Datuk Bandar Kuala Lumpur PEMBELI : ▇▇▇▇▇▇▇▇ Bin ▇▇▇▇▇ @ ▇▇▇▇ BEBANAN : Diserahhak kepada RHB Bank Berhad [196501000373 (6171-M)] Hartanah tersebut terletak di tingkat 9 pada bangunan apartment 14-tingkat terletak di Melati Impian Apartment, Setapak Fasa 1, Kuala Lumpur. Hartanah tersebut adalah sebuah unit apartment 3 ▇▇▇▇▇ dikenali sebaga ▇▇▇▇▇ Pemaju No. 9, Tingkat No.9, Pembangunan dikenali sebagai Melati Impian Apartment Setapak Fasa 1, Kuala Lumpur ▇▇▇ mempunyai alamat surat-▇▇▇▇▇▇▇▇ ▇▇ ▇▇▇▇ ▇▇. ▇-▇, ▇▇▇▇▇▇ Impian Apartment, ▇▇▇▇▇ ▇/▇▇▇, ▇▇▇▇▇ ▇▇▇▇▇▇, ▇▇▇▇▇ ▇▇▇▇▇ ▇▇▇▇▇▇, ▇▇▇▇▇▇▇ ▇▇▇▇▇▇▇▇▇▇▇ ▇▇▇▇▇ ▇▇▇▇▇▇. Harta ini dijual “keadaan seperti mana sediada” dengan harga rizab sebanyak RM 300,000.00 (RINGGIT MALAYSIA: TIGA RATUS RIBU SAHAJA) ▇▇▇ tertakluk kepada syarat-syarat Jualan ▇▇▇ melalui penyerahan hakkan dari Pemegang Serahak, tertakluk kepada kelulusan di perolehi oleh pihak Pembeli daripada pihak berkuasa, jika ada, termasuk semua terma, syarat ▇▇▇ perjanjian yang dikenakan ▇▇▇ mungkin dikenakan oleh Pihak Berkuasa yang berkenaan. Pembeli bertanggungjawab sepenuhnya untuk memperolehi ▇▇▇ mematuhi syarat-syarat berkenaan daripada Pihak Berkuasa yang berkenaan, jika ada ▇▇▇ semua ▇▇▇ ▇▇▇ perbelanjaan ditanggung ▇▇▇ dibayar oleh ▇▇▇▇▇ ▇▇▇▇▇▇▇.Pembeli atas talian (online) juga tertakluk kepada terma-terma ▇▇▇ syarat-syarat terkandung dalam ▇▇▇.▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇.▇▇▇ Pembeli yang berminat adalah dikehendaki mendeposit kepada Pelelong 10% daripada harga rizab dalam bentuk Bank Draf atau Cashier’s Order di atas nama RHB Bank Berhad sebelum lelongan awam ▇▇▇ ▇▇▇▇ ▇▇▇▇ ▇▇▇▇▇▇ hendaklah dibayar dalam tempoh sembilan puluh (90) hari dari tarikh lelongan kepada RHB Bank Berhad melalui Bank Draf / ▇▇▇▇▇▇. Butir-butir pembayaran melalui ▇▇▇▇▇▇, ▇▇▇▇ berhubung dengan Tetuan Zahrin Emrad & Sujaihah. Untuk maklumat lanjut, ▇▇▇▇ berhubung dengan TETUAN ZAHRIN EMRAD & SUJIAHAH, yang beralamat di Suite 10.3, 10th Floor, ▇▇▇ ▇▇▇▇ Building, ▇▇.▇▇, ▇▇▇▇▇ ▇▇▇▇ ▇▇▇▇▇▇, ▇▇▇▇▇ ▇▇▇▇▇ ▇▇▇▇▇▇. Tel: ▇▇-▇▇▇▇ ▇▇▇▇ / Fax: ▇▇-▇▇▇▇ ▇▇▇▇. [ Ruj: ZES/ZHR/RHB-FC/16250-17/0614-pae ], peguamcara bagi pihak pemegang ▇▇▇▇▇ ▇▇▇ atau pelelong yang tersebut dibawah.

  • Dewatering (a) Where the whole of a site is so affected by surface water following a period of rain that all productive work is suspended by agreement of the Parties, then dewatering shall proceed as above with Employees so engaged being paid at penalty rates as is the case for safety rectification work. This work is typically performed by Employees engaged within CW1, CW2 or CW3 classifications. When other Employees are undertaking productive work in an area or areas not so affected then dewatering will only attract single time rates. (b) Where a part of a site is affected by surface water following a period of rain, thus rendering some areas unsafe for productive work, consistent with the Employer’s obligations under the OH&S Act, appropriate Employees shall assist in the tidying up of their own work site or area if it is so affected. Where required, appropriate Employees will be provided with the appropriate PPE. Such work to be paid at single time rates. Productive work will continue in areas not so affected. (c) To avoid any confusion any ‘dewatering’ time which prevents an Employee from being engaged in their normal productive work is not included in any calculation for the purposes of determining whether an Employee is entitled to go home due to wet weather (refer clauses 32.4 and 32.5)

  • Reducing Text Messaging While Driving Pursuant to Executive Order 13513, 74 FR 51225 (Oct. 6, 2009), Recipient should encourage its employees, subrecipients, and contractors to adopt and enforce policies that ban text messaging while driving, and Recipient should establish workplace safety policies to decrease accidents caused by distracted drivers.